scholarly journals Identification of Prognostic Biomarkers of Cutaneous Melanoma Based on Analysis of Tumor Mutation Burden

2020 ◽  
Vol 2020 ◽  
pp. 1-14
Author(s):  
Jiaqiong Lin ◽  
Yan Lin ◽  
Zena Huang ◽  
Xiaoyong Li
Keyword(s):  
Cutaneous Melanoma ◽  
Prognostic Model ◽  
Cox Regression ◽  
Risk Group ◽  
Predictive Biomarkers ◽  
Tumor Mutation Burden ◽  
Skin Development ◽  
Cox Regression Analysis ◽  
Immune Infiltration ◽  
Mutation Burden

Background. Immunotherapy offers a novel approach for the treatment of cutaneous melanoma, but the clinical efficiency varies for individual patients. In consideration of the high cost and adverse effects of immunotherapy, it is essential to explore the predictive biomarkers of outcomes. Recently, the tumor mutation burden (TMB) has been proposed as a predictive prognosticator of the immune response. Method. RNA-seq and somatic mutation datasets of 472 cutaneous melanoma patients were downloaded from The Cancer Genome Atlas (TCGA) database to analyze mutation type and TMB. Differently expressed genes (DEGs) were identified for functional analysis. TMB-related signatures were identified via LASSO and multivariate Cox regression analysis. The association between mutants of signatures and immune cells was evaluated from the TIMER database. Furthermore, the Wilcox test was applied to assess the difference in immune infiltration calculated by the CIBERSORT algorithm in risk groupings. Results. C>T substitutions and TTN were the most common SNV and mutated gene, respectively. Patients with low TMB presented poor prognosis. DEGs were mainly implicated in skin development, cell cycle, DNA replication, and immune-associated crosstalk pathways. Furthermore, a prognostic model consisting of eight TMB-related genes was developed, which was found to be an independent risk factor for treatment outcome. The mutational status of eight TMB-related genes was associated with a low level of immune infiltration. In addition, the immune infiltrates of CD4+ and CD8+ T cells, NK cells, and M1 macrophages were higher in the low-risk group, while those of M0 and M2 macrophages were higher in the high-risk group. Conclusion. Our study demonstrated that a higher TMB was associated with favorable survival outcome in cutaneous melanoma. Moreover, a close association between prognostic model and immune infiltration was identified, providing a new potential target for immunotherapy.

scholarly journals ­ Characterization of the Immune Infiltration Landscape and Identification of Prognostic Biomarkers for Esophageal Cancer

2021 ◽  
Author(s):  
Yuanmei Chen ◽  
Xinyi Huang ◽  
Haiyan Peng ◽  
Guibin Weng ◽  
Zhengrong Huang ◽  
...  
Keyword(s):  
Overall Survival ◽  
Esophageal Cancer ◽  
Prognostic Model ◽  
Cox Regression ◽  
Expression Profiles ◽  
Cancer Genome ◽  
Prognostic Models ◽  
Tumor Mutation Burden ◽  
Cox Regression Analysis ◽  
Mutation Burden

Abstract Background. Esophageal cancer (EC) is the 7th most common neoplasm and the 6th most common cause of cancer-related death worldwide. Immunotherapy is an effective treatment for EC patients. However, there are no dependable markers for predicting prognosis and immunotherapy responses in EC. Our study aims to explore prognostic models and markers in EC as well as predictors for immunotherapy. Methods. The expression profiles of EC were obtained from The Cancer Genome Atlas (TCGA), the Gene Expression Omnibus (GEO), and International Cancer Genome Consortium (ICGC) databases. Cox regression analysis was performed to construct a prognostic model. Overall survival and receiver operating characteristic curve analyses were applied to verify the accuracy of the model. The CIBERSORT algorithm was conducted to quantify the infiltration of different immune cells, and EC was grouped into three immune cell infiltration (ICI) clusters. PD-1 and PD-L1 expressions were compared between the ICI clusters. Overall survival analysis between ICI score and tumor mutation burden was conducted. The immunotherapy response of patients in different ICI score clusters was also compared. The copy number variations, somatic mutations, and single nucleotide polymorphisms were analyzed. Enrichment analyses were also performed. Results. A prognostic model was successfully constructed. Three ICI clusters were identified, and the clusters with high immune and stromal scores tended to have more immune-activated phenotypes and higher expressions of PD-1 and PDL1. The ICI score may be used as a predictor independent of tumor mutation burden. Patients with higher ICI score tended to have better immunotherapeutic responses than those with lower scores. Enrichment analyses showed that the differentially expressed genes were mostly enriched in microvillus and the KRAS and IL6/JAK/STAT3 pathways. The top eight genes with the highest mutation frequencies in EC were identified and all related to the prognosis of EC patients. Conclusions. Our study established an effective prognostic model and identified markers for predicting the prognosis and immunotherapy response of EC patients.

Exploration of Tumor Mutation Burden Combined with Immune Infiltrates in the Prognosis of Lung Adenocarcinoma

2021 ◽  
Author(s):  
Zhenyu Zhao ◽  
Boxue He ◽  
Qidong Cai ◽  
Pengfei Zhang ◽  
Xiong Peng ◽  
...  
Keyword(s):  
High Risk ◽  
Lung Adenocarcinoma ◽  
Clinical Application ◽  
Risk Group ◽  
High Risk Group ◽  
Immune Microenvironment ◽  
Tumor Mutation Burden ◽  
Immune Signature ◽  
Immune Infiltration ◽  
Mutation Burden

Abstract Background: Lung adenocarcinoma (LUAD) accounts for a majority of cancer-related deaths worldwide annually. A recent study shows that immunotherapy is an effective method of LUAD treatment, and tumor mutation burden (TMB) was associated with the immune microenvironment and affected the immunotherapy. Exploration of the gene signature associated with tumor mutation burden and immune infiltrates in predicting prognosis in lung adenocarcinoma in this study, we explored the correlation of TMB with immune infiltration and prognosis in LUAD.Materials and Methods: In this study, we firstly got mutation data and LUAD RNA-Seq data of the LUAD from The Cancer Genome Atlas (TCGA), and according to the TMB we divided the patients into high/low-TMB levels groups. The gene ontology (GO) pathway enrichment analysis and KOBAS-Kyoto Encyclopedia of Genes and Genomes (KEGG) pathways analysis were utilized to explore the molecular function of the differentially expressed genes (DEGs) between the two groups. The function enrichment analyses of DEGs were related to the immune pathways. Then, the ESTIMATE algorithm, CIBERSORT, and ssGSEA analysis were utilized to identify the relationship between TMB subgroups and immune infiltration. According to the results, Venn analysis was utilized to select the immune-related genes in DEGs. Univariate and Lasso Cox proportional hazards regression analyses were performed to construct the signature which positively associated with the immune infiltration and affected the survival. Finally, we verified the correlation between the signature and immune infiltration. Result: The exploration of the immune infiltration suggested that high-TMB subgroups positively associated with the high level of immune infiltration in LUAD patients. According to the TMB-related immune signature, the patients were divided into High/Low-risk groups, and the high-risk group was positively associated with poor prognostic. The results of the PCA analysis confirmed the validity of the signature. We also verified the effectiveness of the signature in GSE30219 and GSE72094 datasets. The ROC curves and C-index suggested the good clinical application of the TMB-related immune signature in LUAD prognosis. Another result suggested that the patients of the high-risk group were positively associated with higher TMB levels, PD-L1expression, and immune infiltration levels.Conclusion: In conclusion, our signature provides potential biomarkers for studying aspects of the TMB in LUAD such as TMB affected immune microenvironment and prognosis. This signature may provide some biomarkers which could improve the biomarkers of PD-L1 immunotherapy response and were inverted for the clinical application of the TMB in LUAD. LUAD male patients with higher TMB-levels and risk scores may benefit from immunotherapy. The high-risk patients along with higher PD-L1 expression of the signature may suitable for immunotherapy and improve their survival by detecting the TMB of LUAD.

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 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.

scholarly journals Prognostic Modeling of Patients with Metastatic Melanoma Based on Tumor Immune Microenvironment Characteristics

2021 ◽  
Author(s):  
Jing Liu ◽  
Ting Ye ◽  
Xue fang Zhang ◽  
Yong jian Dong ◽  
Wen feng Zhang ◽  
...  
Keyword(s):  
High Risk ◽  
Metastatic Melanoma ◽  
Prognostic Model ◽  
Cox Regression ◽  
Risk Group ◽  
Low Risk ◽  
Risk Scores ◽  
Immune Microenvironment ◽  
Immune Infiltration ◽  
Tumor Immune Microenvironment

Abstract Most of the malignant melanomas are already in the middle and advanced stages when they are diagnosed, which is often accompanied by the metastasis and spread of other organs.Besides, the prognosis of patients is bleak. The characteristics of the local immune microenvironment in metastatic melanoma have important implications for both tumor progression and tumor treatment. In this study, data on patients with metastatic melanoma from the TCGA and GEO datasets were selected for immune, stromal, and estimate scores, and overlapping differentially expressed genes (DEGs) were screened. A nine-IRGs prognostic model (ALOX5AP, ARHGAP15, CCL8, FCER1G, GBP4, HCK, MMP9, RARRES2 and TRIM22) was established by univariate COX regression, LASSO and multivariate COX regression. Receiver operating characteristic (ROC) curves were used to test the predictive accuracy of the model. Immune infiltration was analyzed by using CIBERSORT, Xcell and ssGSEA in high-risk and low-risk groups. The immune infiltration of the high-risk group was significantly lower than that of the low-risk group. Immune checkpoint analysis revealed that the expression of PDCD1, CTLA4, TIGIT, CD274, HAVR2 and LAG3 were significantly different in groups with different levels of risk scores. WGCNA analysis found that the yellow-green module contained seven genes (ALOX5AP, FCER1G, GBP4, HCK, MMP9, RARRES2 and TRIM22) from the nine-IRG prognostic model, of which the yellow-green module had the highest correlation with risk scores. The results of GO and KEGG suggested that the genes in the yellow-green module were mainly enriched in immune-related biological processes. Finally, we analyzed the prognostic ability and expression characteristics of ALOX5AP, ARHGAP15, CCL8, FCER1G, GBP4, HCK, MMP9, RARRES2 and TRIM22 in metastatic melanoma. Overall, a prognostic model for metastatic melanoma based on the characteristics of the tumor immune microenvironment was established, which was helpful for further studies.It could function well in helping people to understand the characteristics of the immune microenvironment in metastatic melanoma and to find possible therapeutic targets.

scholarly journals Identification of Potential Prognostic Biomarkers Associated With Cancerometastasis in Skin Cutaneous Melanoma

2021 ◽  
Vol 12 ◽  
Author(s):  
Yang Li ◽  
Shanshan Lyu ◽  
Zhe Gao ◽  
Weifeng Zha ◽  
Ping Wang ◽  
...  
Keyword(s):  
Cutaneous Melanoma ◽  
Immune Cell ◽  
Cox Regression ◽  
Predictive Biomarkers ◽  
Survival Prediction ◽  
Differential Analysis ◽  
Hub Genes ◽  
Cox Regression Analysis ◽  
Aggressive Tumor ◽  
Six Genes

Skin cutaneous melanoma (SKCM) is a highly aggressive tumor. The mortality and drug resistance among it are high. Thus, exploring predictive biomarkers for prognosis has become a priority. We aimed to find immune cell-based biomarkers for survival prediction. Here 321 genes were differentially expressed in immune-related groups after ESTIMATE analysis and differential analysis. Two hundred nineteen of them were associated with the metastasis of SKCM via weighted gene co-expression network analysis. Twenty-six genes in this module were hub genes. Twelve of the 26 genes were related to overall survival in SKCM patients. After a multivariable Cox regression analysis, we obtained six of these genes (PLA2G2D, IKZF3, MS4A1, ZC3H12D, FCRL3, and P2RY10) that were independent prognostic signatures, and a survival model of them performed excellent predictive efficacy. The results revealed several essential genes that may act as significant prognostic factors of SKCM, which could deepen our understanding of the metastatic mechanisms and improve cancer treatment.

scholarly journals Development and Validation of a Novel Hypoxia-Related Long Noncoding RNA Model With Regard to Prognosis and Immune Features in Breast Cancer

2021 ◽  
Vol 9 ◽  
Author(s):  
Peng Gu ◽  
Lei Zhang ◽  
Ruitao Wang ◽  
Wentao Ding ◽  
Wei Wang ◽  
...  
Keyword(s):  
Breast Cancer ◽  
High Risk ◽  
Long Noncoding Rna ◽  
Noncoding Rna ◽  
Prognostic Model ◽  
Cox Regression ◽  
Risk Group ◽  
High Risk Group ◽  
Low Risk ◽  
Cox Regression Analysis

Background: Female breast cancer is currently the most frequently diagnosed cancer in the world. This study aimed to develop and validate a novel hypoxia-related long noncoding RNA (HRL) prognostic model for predicting the overall survival (OS) of patients with breast cancer.Methods: The gene expression profiles were downloaded from The Cancer Genome Atlas (TCGA) database. A total of 200 hypoxia-related mRNAs were obtained from the Molecular Signatures Database. The co-expression analysis between differentially expressed hypoxia-related mRNAs and lncRNAs based on Spearman’s rank correlation was performed to screen out 166 HRLs. Based on univariate Cox regression and least absolute shrinkage and selection operator Cox regression analysis in the training set, we filtered out 12 optimal prognostic hypoxia-related lncRNAs (PHRLs) to develop a prognostic model. Kaplan–Meier survival analysis, receiver operating characteristic curves, area under the curve, and univariate and multivariate Cox regression analyses were used to test the predictive ability of the risk model in the training, testing, and total sets.Results: A 12-HRL prognostic model was developed to predict the survival outcome of patients with breast cancer. Patients in the high-risk group had significantly shorter median OS, DFS (disease-free survival), and predicted lower chemosensitivity (paclitaxel, docetaxel) compared with those in the low-risk group. Also, the risk score based on the expression of the 12 HRLs acted as an independent prognostic factor. The immune cell infiltration analysis revealed that the immune scores of patients in the high-risk group were lower than those of the patients in the low-risk group. RT-qPCR assays were conducted to verify the expression of the 12 PHRLs in breast cancer tissues and cell lines.Conclusion: Our study uncovered dozens of potential prognostic biomarkers and therapeutic targets related to the hypoxia signaling pathway in breast cancer.

scholarly journals Identification of a Gene Prognostic Model of Gastric Cancer Based on Analysis of Tumor Mutation Burden

2021 ◽  
Vol 27 ◽  
Author(s):  
Weijun Ma ◽  
Weidong Li ◽  
Lei Xu ◽  
Lu Liu ◽  
Yu Xia ◽  
...  
Keyword(s):  
Gastric Cancer ◽  
Prognostic Model ◽  
Cox Regression ◽  
The Cancer Genome Atlas ◽  
Survival Outcomes ◽  
Activated T Cells ◽  
Single Nucleotide ◽  
Cox Regression Analysis ◽  
Immune Infiltration ◽  
Surgical Methods

Introduction: Gastric cancer is one of the most common cancers. Although some progress has been made in the treatment of gastric cancer with the improvement of surgical methods and the application of immunotherapy, the prognosis of gastric cancer patients is still unsatisfactory. In recent years, there has been increasing evidence that tumor mutational load (TMB) is strongly associated with survival outcomes and response to immunotherapy. Given the variable response of patients to immunotherapy, it is important to investigate clinical significance of TMB and explore appropriate biomarkers of prognosis in patients with gastric cancer (GC).Material and Methods: All data of patients with gastric cancer were obtained from the database of The Cancer Genome Atlas (TCGA). Samples were divided into two groups based on median TMB. Differently expressed genes (DEGs) between the high- and low-TMB groups were identified and further analyzed. We identified TMB-related genes using Lasso, univariate and multivariate Cox regression analysis and validated the survival result of 11 hub genes using Kaplan-Meier Plotter. In addition, “CIBERSORT” package was utilized to estimate the immune infiltration.Results: Single nucleotide polymorphism (SNP), C &gt; T transition were the most common variant type and single nucleotide variant (SNV), respectively. Patients in the high-TMB group had better survival outcomes than those in the low-TMB group. Besides, eleven TMB-related DEGs were utilized to construct a prognostic model that could be an independent risk factor to predict the prognosis of patients with GC. What’s more, the infiltration levels of CD4+ memory-activated T cells, M0 and M1 macrophages were significantly increased in the high-TMB group compared with the low-TMB group.Conclusions: Herein, we found that patients with high TMB had better survival outcomes in GC. In addition, higher TMB might promote immune infiltration, which could provide new ideas for immunotherapy.

Construction and Validation of Prognostic Markers of Liver Cancer Based on Autophagy Genes

2021 ◽  
Vol 21 ◽  
Author(s):  
Dawei Zhou ◽  
Junchen Wan ◽  
Jiang Luo ◽  
Yuhao Tao
Keyword(s):  
Regression Analysis ◽  
High Risk ◽  
Liver Cancer ◽  
Cancer Patients ◽  
Prognostic Model ◽  
Cox Regression ◽  
Risk Group ◽  
Primary Liver Cancer ◽  
Risk Groups ◽  
Cox Regression Analysis

Background: Liver cancer is one of the most common diseases in the world. At present, the mechanism of autophagy genes in liver cancer is not very clear. Therefore, it is meaningful to study the role and prognostic value of autophagy genes in liver cancer. Objective: The purpose of this study is to conduct a bioinformatics analysis of autophagy genes related to primary liver cancer to establish a prognostic model of primary liver cancer based on autophagy genes. Results: Through difference analysis, 31 differential autophagy genes were screened out and then analyzed by GO and KEGG analysis. At the same time, we built a PPI network. To optimize the evaluation of the prognosis of liver cancer patients, we integrated multiple autophagy genes to establish a prognostic model. By using univariate cox regression analysis, 15 autophagy genes related to prognosis were screened out. Then we included these 15 genes into the Least Absolute Shrinkage and Selection Operator (LASSO), and performed multi-factor cox regression analysis on the 9 selected genes to construct a prognostic model. The risk score of each patient was calculated based on 4 genes(BIRC5, HSP8, SQSTM1, and TMEM74) which participated in the establishing of the model, then the patients were divided into high-risk groups and low-risk groups. In the multivariate cox regression analysis, the risk score was the independent prognostic factors (HR=1.872, 95%CI=1.544-2.196, P<0.001). Survival analysis showed that the survival time of the low-risk group was significantly longer than that of the high-risk group. Combining clinical characteristics and autophagy genes, we constructed a nomogram for predicting prognosis. The external dataset GSE14520 proved that the nomogram has a good prediction for individual patients with primary liver cancer. Conclusion: This study provided potential autophagy-related markers for liver cancer patients to predict their prognosis and revealed part of the molecular mechanism of liver cancer autophagy. At the same time, the certain gene pathways and protein pathways related to autophagy may provide some inspiration for the development of anticancer drugs.

scholarly journals Pyroptosis-Related Gene Signatures Can Robustly Diagnose Skin Cutaneous Melanoma and Predict the Prognosis

2021 ◽  
Vol 11 ◽  
Author(s):  
Anji Ju ◽  
Jiaze Tang ◽  
Shuohua Chen ◽  
Yan Fu ◽  
Yongzhang Luo
Keyword(s):  
Cutaneous Melanoma ◽  
Prognostic Model ◽  
Cox Regression ◽  
Machine Learning Algorithms ◽  
Gene Set Enrichment Analysis ◽  
Functional Enrichment ◽  
Support Vector ◽  
K Nearest Neighbor ◽  
Cox Regression Analysis ◽  
Diagnosis And Prognosis

Skin cutaneous melanoma (SKCM) is a chronically malignant tumor with a high mortality rate. Pyroptosis, a kind of pro-inflammatory programmed cell death, has been linked to cancer in recent studies. However, the value of pyroptosis in the diagnosis and prognosis of SKCM is not clear. In this study, it was discovered that 20 pyroptosis-related genes (PRGs) differed in expression between SKCM and normal tissues, which were related to diagnosis and prognosis. Firstly, based on these genes, nine machine-learning algorithms were shown to perform well in constructing diagnostic classifiers, including K-Nearest Neighbor (KNN), logistic regression, Support Vector Machine (SVM), Artificial Neural Network (ANN), decision tree, random forest, XGBoost, LightGBM, and CatBoost. Secondly, the least absolute shrinkage and selection operator (LASSO) Cox regression analysis was applied and the prognostic model was constructed based on 9 PRGs. Subgroups in low and high risks determined by the prognostic model were shown to have different survival. Thirdly, functional enrichment analyses were performed by applying the gene set enrichment analysis (GSEA), and results suggested that the risk was related to immune response. In conclusion, the expression signatures of pyroptosis-related genes are effective and robust in the diagnosis and prognosis of SKCM, which is related to immunity.

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