KIAA0101 in Malignant Pleural Mesothelioma: a Potential Diagnostic and Prognostic Marker

2021 ◽  
Vol 24 ◽  
Author(s):  
Ping Lin ◽  
Yuean Zhao ◽  
Xiaoqian Li ◽  
Zongan Liang
Keyword(s):  
Gene Expression ◽  
Regression Analysis ◽  
Malignant Pleural Mesothelioma ◽  
Cox Regression ◽  
Expression Profiles ◽  
Gene Set Enrichment Analysis ◽  
High Expression ◽  
Pleural Mesothelioma ◽  
Hub Genes ◽  
Cox Regression Analysis

Background: Currently, there are no reliable diagnostic and prognostic markers for malignant pleural mesothelioma (MPM). The objective of this study was to identify hub genes that could be helpful for diagnosis and prognosis in MPM by using bioinformatics analysis. Materials and Methods: The gene expression profiles were downloaded from the Gene Expression Omnibus (GEO) database and The Cancer Genome Atlas (TCGA). Weighted gene co-expression network analysis (WGCNA), LASSO regression analysis, Cox regression analysis, and Gene Set Enrichment Analysis (GSEA) were performed to identify hub genes and their functions. Results: A total of 430 up-regulated and 867 downregulated genes in MPM were identified based on the GSE51024 dataset. According to the WGCNA analysis, differentially expressed genes were classified into 8 modules. Among them, the pink module was most closely associated with MPM. According to genes with GS > 0.8 and MM > 0.8, six genes were selected as candidate hub genes (NUSAP1, TOP2A, PLOD2, BUB1B, UHRF1, KIAA0101) in the pink module. In the LASSO model, three genes (NUSAP1, PLOD2, and KIAA0101) were identified with non-zero regression coefficients and were considered hub genes among the 6 candidates. The hub gene-based LASSO model can accurately distinguish MPM from controls (AUC = 0.98). Moreover, the high expression level of KIAA0101, PLOD2, and NUSAP1 were all associated with poor prognosis compared to the low level in Kaplan–Meier survival analyses. After further multivariate Cox analysis, only KIAA0101 (HR = 1.55, 95% CI = 1.05-2.29) was identified as an independent prognostic factor among these hub genes. Finally, GSEA revealed that high expression of KIAA0101 was closely associated with 10 signaling pathways. Conclusion: Our study identified several hub genes relevant to MPM, including NUSAP1, PLOD2, and KIAA0101. Among these genes, KIAA0101 appears to be a useful diagnostic and prognostic biomarker for MPM, which may provide new clues for MPM diagnosis and therapy.

scholarly journals Expression of LOX Suggests Poor Prognosis in Gastric Cancer

2021 ◽  
Vol 8 ◽  
Author(s):  
Jinfeng Zhu ◽  
Chen Luo ◽  
Jiefeng Zhao ◽  
Xiaojian Zhu ◽  
Kang Lin ◽  
...  
Keyword(s):  
Gene Expression ◽  
Gastric Cancer ◽  
Logistic Regression ◽  
Regression Analysis ◽  
Poor Prognosis ◽  
Cox Regression ◽  
Gene Set Enrichment Analysis ◽  
High Expression ◽  
Expression Level ◽  
Cox Regression Analysis

Background: Lysyl oxidase (LOX) is a key enzyme for the cross-linking of collagen and elastin in the extracellular matrix. This study evaluated the prognostic role of LOX in gastric cancer (GC) by analyzing the data of The Cancer Genome Atlas (TCGA) and the Gene Expression Omnibus (GEO) dataset.Methods: The Wilcoxon rank-sum test was used to calculate the expression difference of LOX gene in gastric cancer and normal tissues. Western blot and immunohistochemical staining were used to evaluate the expression level of LOX protein in gastric cancer. Kaplan-Meier analysis was used to calculate the survival difference between the high expression group and the low expression group in gastric cancer. The relationship between statistical clinicopathological characteristics and LOX gene expression was analyzed by Wilcoxon or Kruskal-Wallis test and logistic regression. Univariate and multivariate Cox regression analysis was used to find independent risk factors affecting the prognosis of GC patients. Gene set enrichment analysis (GSEA) was used to screen the possible mechanisms of LOX and GC. The CIBERSORT calculation method was used to evaluate the distribution of tumor-infiltrating immune cell (TIC) abundance.Results: LOX is highly expressed in gastric cancer tissues and is significantly related to poor overall survival. Wilcoxon or Kruskal-Wallis test and Logistic regression analysis showed, LOX overexpression is significantly correlated with T-stage progression in gastric cancer. Multivariate Cox regression analysis on TCGA and GEO data found that LOX (all p < 0.05) is an independent factor for poor GC prognosis. GSEA showed that high LOX expression is related to ECM receptor interaction, cancer, Hedgehog, TGF-beta, JAK-STAT, MAPK, Wnt, and mTOR signaling pathways. The expression level of LOX affects the immune activity of the tumor microenvironment in gastric cancer.Conclusion: High expression of LOX is a potential molecular indicator for poor prognosis of gastric cancer.

scholarly journals A six-gene-based signature for breast cancer radiotherapy sensitivity prediction

2020 ◽  
Author(s):  
Xing Chen ◽  
Junjie Zheng ◽  
Min ling Zhuo ◽  
Ailong Zhang ◽  
Zhenhui You
Keyword(s):  
Breast Cancer ◽  
Gene Expression ◽  
Regression Analysis ◽  
Cox Regression ◽  
Expression Profiles ◽  
Gene Expression Profiles ◽  
Cancer Genome ◽  
The Cancer Genome Atlas ◽  
Cox Regression Analysis ◽  
Breast Cancer Radiotherapy

Abstract Background: Breast cancer (BRCA) represents the most common malignancy among women worldwide that with high mortality. Radiotherapy is a prevalent therapeutic for BRCA that with heterogeneous effectiveness among patients. Methods: we proposed to develop a gene expression-based signature for BRCA radiotherapy sensitivity prediction. Gene expression profiles of BRCA samples from the Cancer Genome Atlas (TCGA) and International Cancer Genome Consortium (ICGC) were obtained and used as training and independent testing dataset, respectively. Differential expression genes (DEGs) in BRCA tumor samples compared with their paracancerous samples in the training set were identified by using edgeR Bioconductor package followed by dimensionality reduction through autoencoder method and univariate Cox regression analysis to screen genes among DEGs that with significant prognosis significance in patients that were previously treated with radiation. LASSO Cox regression method was applied to screen optimal genes for constructing radiotherapy sensitivity prediction signature. Results: 603 DEGs were obtained in BRCA tumor samples, and seven out of which were retained after univariate cox regression analysis. LASSO Cox regression analysis finally remained six genes based on which the radiotherapy sensitivity prediction model was constructed. The signature was proved to be robust in both training and independent testing sets and an independent marker for BRCA radiotherapy sensitivity prediction. Conclusions: this study should be helpful for BRCA patients’ therapeutics selection and clinical decision.

scholarly journals Metabolic genes show excellent prognostic ability for clear cell renal cell carcinoma

2020 ◽  
Author(s):  
Chengjian Ji ◽  
Yichun Wang ◽  
Liangyu Yao ◽  
Jiaochen Luan ◽  
Rong Cong ◽  
...  
Keyword(s):  
Gene Expression ◽  
Renal Cell Carcinoma ◽  
Regression Analysis ◽  
Cell Carcinoma ◽  
Renal Cell ◽  
Cox Regression ◽  
Expression Profiles ◽  
Cell Renal Cell Carcinoma ◽  
Cox Regression Analysis ◽  
Metabolic Genes

Abstract Background Renal cell carcinoma (RCC) is one of the major malignant tumors of the urinary system, with a high mortality rate and a poor prognosis. Clear cell renal cell carcinoma (ccRCC) is the most common subtype of RCC. Although the diagnosis and treatment methods have been significantly improved, the incidence and mortality of ccRCC are high and still increasing. The occurrence and development of ccRCC are closely related to the changes of classic metabolic pathways. This article aims to explore the relationship between metabolic genes and the prognosis of patients with ccRCC. Patients and methods: Gene expression profiles of 63 normal kidney tissues and 446 ccRCC tissues from TCGA database and gene expression profiles of 39 ccRCC tissues from GEO database were used to obtain differentially expressed genes (DEGs) in ccRCC. Through the the KEGG gene sets of GSEA database, we obtained metabolic genes (MGs). Univariate Cox regression analysis was used to identify prognostic MGs. Lasso regression analysis was used to eliminate false positives because of over-fitting. Multivariate Cox regression analysis was used to established a prognostic model. Gene expression data and related survival data of 101 ccRCC patients from ArrayExpress database were used for external validation. Survival analysis, ROC curve analysis, independent prognostic analysis and clinical correlation analysis were performed to evaluate this model. Results We found that there were 479 abnormally expressed MGs in ccRCC tissues. Through univariate Cox regression analysis, Lasso regression analysis and multivariate Cox regression analysis, we identified 4 prognostic MGs (P4HA3, ETNK2, PAFAH2 and ALAD) and established a prognostic model (riskScore). Whether in the training cohort, the testing cohort or the entire cohort, this model could accurately stratify patients with different survival outcomes. The prognostic value of riskScore and 4 MGs was also confirmed in the ArrayExpress database. Results of GSEA analysis show that DEGs in patients with better prognosis were enriched in metabolic pathways. Then, a new Nomogram with higher prognostic value was constructed to better predict the 1-year OS, 3-year OS and 5-year OS of ccRCC patients. In addition, we successfully established a ceRNA network to further explain the differences in the expression of these MGs between high-risk patients and low-risk patients Conclusion We have successfully established a risk model (riskScore) based on 4 MGs, which could accurately predict the prognosis of patients with ccRCC. Our research may shed new light on ccRCC patients' prognosis and treatment management.

scholarly journals Identification of 4-genes model in papillary renal cell tumor microenvironment based on comprehensive analysis

BMC Cancer ◽  
2021 ◽  
Vol 21 (1) ◽  
Author(s):  
Liang Luo ◽  
Haiyi Zhou ◽  
Hao Su
Keyword(s):  
Gene Expression ◽  
Regression Analysis ◽  
Tumor Microenvironment ◽  
Renal Cell ◽  
Cox Regression ◽  
Ppi Network ◽  
Hub Genes ◽  
Cox Regression Analysis ◽  
Venn Diagrams ◽  
Kaplan Meier

Abstract Background The tumor microenvironment acts a pivotal part in the occurrence and development of tumor. However, there are few studies on the microenvironment of papillary renal cell carcinoma (PRCC). Our study aims to explore prognostic genes related to tumor microenvironment in PRCC. Methods PRCC expression profiles and clinical data were extracted from The Cancer Gene Atlas (TCGA) and Gene Expression Omnibus (GEO) database. Immune/stromal scores were performed utilizing the ESTIMATE algorithm. Three hundred fifty-seven samples were split into two groups on the basis of median immune/stromal score, and comparison of gene expression was conducted. Intersect genes were obtained by Venn diagrams. Hub genes were selected through protein-protein interaction (PPI) network construction, and relevant functional analysis was conducted by DAVID. We used Kaplan–Meier analysis to identify the correlations between genes and overall survival (OS) and progression-free survival (PFS). Univariate and multivariate cox regression analysis were employed to construct survival model. Cibersort was used to predict the immune cell composition of high and low risk group. Combined nomograms were built to predict PRCC prognosis. Immune properties of PRCC were validated by The Cancer Immunome Atlas (TCIA). Results We found immune/stromal score was correlated with T pathological stages and PRCC subtypes. Nine hundred eighty-nine differentially expressed genes (DEGs) and 1169 DEGs were identified respectively on the basis of immune and stromal score. Venn diagrams indicated that 763 co-upregulated genes and 4 co-downregulated genes were identified. Kaplan-Meier analysis revealed that 120 genes were involved in tumor prognosis. Then PPI network analysis identified 22 hub genes, and four of which were significantly related to OS in patients with PRCC confirmed by cox regression analysis. Finally, we constructed a prognostic nomogram which combined with influence factors. Conclusions Four tumor microenvironment-related genes (CD79A, CXCL13, IL6 and CCL19) were identified as biomarkers for PRCC prognosis.

scholarly journals A Novel 8-Gene Prognostic Signature for Survival Prediction of Uveal Melanoma

2021 ◽  
Vol 2021 ◽  
pp. 1-18
Author(s):  
Zhongjun Tang ◽  
Kebo Cai
Keyword(s):  
Gene Expression ◽  
Regression Analysis ◽  
Uveal Melanoma ◽  
Cox Regression ◽  
Gene Expression Signature ◽  
Survival Prediction ◽  
Hub Genes ◽  
Data Set ◽  
Cox Regression Analysis ◽  
Expression Signature

Background. Uveal melanoma (UM) has favorable local tumor control, but once metastasis develops, the prognosis is rather poor. Thus, it is urgent to develop metastasis predicting markers. Objective. Our study investigated a novel gene expression-based signature in predicting metastasis for patients with UM. Methods. In the discovery phase, 63 patients with UM from GEO data set GSE22138 were analyzed using the Weighted Correlation Network Analysis (WGCNA) to identify metastasis-related hub genes. The Least Absolute Shrinkage and Selection Operator (Lasso) Cox regression was used to select candidate genes and build a gene expression signature. In the validation phase, the signature was validated in The Cancer Genome Atlas database. Results. Forty-one genes were identified as hub genes of metastasis by WGCNA. After the Lasso Cox regression analysis, eight genes including RPL10A, EIF1B, TIPARP, RPL15, SLC25A38, PHLDA1, TFDP2, and MEGF10 were highlighted as candidate predictors. The gene expression signature for UM (UMPS) could independently predict MFS by univariate and multivariate Cox regression analysis. Incorporating UMPS increased the AUC of the traditional clinical model. In the validation cohort, UMPS performed well in predicting the MFS of UM patients. Conclusions. UMPS, an eight-gene-based signature, is useful in predicting prognosis for patients with UM.

scholarly journals Elevated CDK5R1 predicts worse prognosis in hepatocellular carcinoma based on TCGA data

2020 ◽  
Author(s):  
Zhili Zeng ◽  
Zebiao Cao ◽  
Enxin Zhang ◽  
Haifu Huang ◽  
Ying Tang
Keyword(s):  
Hepatocellular Carcinoma ◽  
Regression Analysis ◽  
Cox Regression ◽  
Hazard Ratio ◽  
Gene Set Enrichment Analysis ◽  
High Expression ◽  
Rapid Progression ◽  
Raw Data ◽  
Cox Regression Analysis ◽  
Free Interval

Background: Hepatocellular carcinoma (HCC) is a malignant tumor with rapid progression, high recurrence rate and poor prognosis. The objective of our investigation was to explore the prognostic value of CDK5R1 in HCC. Methods: The raw data of HCC raw data were downloaded from The Cancer Genome Atlas (TCGA) database. The Wilcoxon signed-rank test, Kruskal-Wallis test and logistic regression were applied to investigate the relevance between the CDK5R1 expression and clinicopathologic characteristics in HCC. Kaplan-Meier and Cox regression analysis were employed to examine the association between clinicopathologic features and survival. Gene set enrichment analysis (GSEA) was applied to annotate the biological function of CDK5R1. Results: CDK5R1 was highly expressed in HCC tissues. The high expression of CDK5R1 in HCC tissues was significantly associated with tumor status (P=0.00), new tumor event (P=0.00), clinical stage (P=0.00), topography (P=0.00). Elevated CDK5R1 had significant correlation with worse overall survival (OS) (P=7.414e−04), disease-specific survival (DSS) (P=5.642e−04), disease-free interval (DFI) (P=1.785e−05), and progression-free interval (PFI) (P=2.512e−06). Besides, univariate and multivariate Cox regression analysis uncovered that increased CDK5R1 can independently predict adverse OS (P=0.037, hazard ratio [HR]=1.7 (95% CI [1.0-2.7])), DFI (P=0.007, hazard ratio [HR]=3.0 (95% CI [1.4-6.7])), PFI (P=0.007, hazard ratio [HR]=2.8 (95% CI [1.3-5.9])). GSEA disclosed that notch signaling pathway and non-small cell lung cancer were prominently enriched in CDK5R1 high expression phenotype. Conclusions: Increased CDK5R1 may act as a promising independent prognostic factor of poor survival in HCC.

scholarly journals Construction and Validation of a Reliable Six-Gene Prognostic Signature Based on the TP53 Alteration for Hepatocellular Carcinoma

2021 ◽  
Vol 11 ◽  
Author(s):  
Junyu Huo ◽  
Liqun Wu ◽  
Yunjin Zang
Keyword(s):  
Gene Expression ◽  
Hepatocellular Carcinoma ◽  
Regression Analysis ◽  
Cox Regression ◽  
Enrichment Analysis ◽  
Gene Set Enrichment Analysis ◽  
Prognostic Signature ◽  
Gene Set Enrichment ◽  
Cox Regression Analysis ◽  
Gene Set

BackgroundThe high mutation rate of TP53 in hepatocellular carcinoma (HCC) makes it an attractive potential therapeutic target. However, the mechanism by which TP53 mutation affects the prognosis of HCC is not fully understood.Material and ApproachThis study downloaded a gene expression profile and clinical-related information from The Cancer Genome Atlas (TCGA) database and the international genome consortium (ICGC) database. We used Gene Set Enrichment Analysis (GSEA) to determine the difference in gene expression patterns between HCC samples with wild-type TP53 (n=258) and mutant TP53 (n=116) in the TCGA cohort. We screened prognosis-related genes by univariate Cox regression analysis and Kaplan–Meier (KM) survival analysis. We constructed a six-gene prognostic signature in the TCGA training group (n=184) by Lasso and multivariate Cox regression analysis. To assess the predictive capability and applicability of the signature in HCC, we conducted internal validation, external validation, integrated analysis and subgroup analysis.ResultsA prognostic signature consisting of six genes (EIF2S1, SEC61A1, CDC42EP2, SRM, GRM8, and TBCD) showed good performance in predicting the prognosis of HCC. The area under the curve (AUC) values of the ROC curve of 1-, 2-, and 3-year survival of the model were all greater than 0.7 in each independent cohort (internal testing cohort, n = 181; TCGA cohort, n = 365; ICGC cohort, n = 229; whole cohort, n = 594; subgroup, n = 9). Importantly, by gene set variation analysis (GSVA) and the single sample gene set enrichment analysis (ssGSEA) method, we found three possible causes that may lead to poor prognosis of HCC: high proliferative activity, low metabolic activity and immunosuppression.ConclusionOur study provides a reliable method for the prognostic risk assessment of HCC and has great potential for clinical transformation.

scholarly journals Identification of a Ubiquitination-Related Gene Risk Model for Predicting Survival in Patients With Pancreatic Cancer

2020 ◽  
Vol 11 ◽  
Author(s):  
Hao Zuo ◽  
Luojun Chen ◽  
Na Li ◽  
Qibin Song
Keyword(s):  
Pancreatic Cancer ◽  
Regression Analysis ◽  
Cancer Patients ◽  
Risk Model ◽  
Cox Regression ◽  
Expression Profiles ◽  
Gene Set Enrichment Analysis ◽  
The Cancer Genome Atlas ◽  
Transcriptional Level ◽  
Cox Regression Analysis

Pancreatic cancer is known as “the king of cancer,” and ubiquitination/deubiquitination-related genes are key contributors to its development. Our study aimed to identify ubiquitination/deubiquitination-related genes associated with the prognosis of pancreatic cancer patients by the bioinformatics method and then construct a risk model. In this study, the gene expression profiles and clinical data of pancreatic cancer patients were downloaded from The Cancer Genome Atlas (TCGA) database and the Genotype-tissue Expression (GTEx) database. Ubiquitination/deubiquitination-related genes were obtained from the gene set enrichment analysis (GSEA). Univariate Cox regression analysis was used to identify differentially expressed ubiquitination-related genes selected from GSEA which were associated with the prognosis of pancreatic cancer patients. Using multivariate Cox regression analysis, we detected eight optimal ubiquitination-related genes (RNF7, NPEPPS, NCCRP1, BRCA1, TRIM37, RNF25, CDC27, and UBE2H) and then used them to construct a risk model to predict the prognosis of pancreatic cancer patients. Finally, the eight risk genes were validated by the Human Protein Atlas (HPA) database, the results showed that the protein expression level of the eight genes was generally consistent with those at the transcriptional level. Our findings suggest the risk model constructed from these eight ubiquitination-related genes can accurately and reliably predict the prognosis of pancreatic cancer patients. These eight genes have the potential to be further studied as new biomarkers or therapeutic targets for pancreatic cancer.

scholarly journals Identification of Two Novel Genes SNX10 and PTGDS with Cervical Squamous Cell Carcinoma Prognosis

2020 ◽  
Author(s):  
Pinping Jiang ◽  
Ying Cao ◽  
Feng Gao ◽  
Wei Sun ◽  
Jinhui Liu ◽  
...  
Keyword(s):  
Functional Analysis ◽  
Regression Analysis ◽  
Cox Regression ◽  
Trichostatin A ◽  
Expression Profiles ◽  
Ppi Network ◽  
Methylation Analysis ◽  
Hub Genes ◽  
Cox Regression Analysis ◽  
Tumor Tissues

Abstract Background: Cervical cancer (CC) is the primary cause of death in women. This study sought to investigate the therapeutic targets of CC. Methods: We downloaded four gene expression profiles from GEO. The RRA method was used to integrate and screen DEGs between CC and normal samples. Functional analysis was performed by clusterprofiler. We built PPI network by STRING and selected hub modules via MCODE. CMap was used to find molecules with therapeutic potential for CC. We also validated hub genes in GEO datasets, GEPIA, immunohistochemistry. Cox regression analysis, TCGA methylation analysis and ONCOMINE were carried out. ROC curve analysis and GSEA were also done to dig out the significance of hub genes. Results: Functional analysis revealed that DEGs were significantly enriched in binding, cell proliferation, transcriptional activity and cell cycle regulation. PPI network screened 30 prominent proteins, with CDK1 having the strongest association with CC. Cmap showed that apigenin, thioguanine and trichostatin A might be used to treat CC. Eight genes were screened out through GEPIA. Of them, only PTGDS and SNX10 have not been reported in CC related articles. The validation in GEO showed that PTGDS showed low expression in tumor tissues while SNX10 showed high expression in tumor tissues. Their expression profiles were consistent with the results in immunohistochemistry. They can distinguish CC and normal tissue and have good diagnostic efficiency. GSEA showed that the two genes were associated with the chemokine signaling pathway. TCGA methylation analysis showed that patients with low-expressed and hyper-methylated PTGDS had a bad prognosis than the patients with high-expressed and hypo-methylated PTGDS. Cox regression analysis showed that SNX10 and PTGDS were independent prognostic indicators for OS among CC patients. Conclusions: In conclusion, PTGDS and SNX10 showed abnormal expression and methylation in CC. Both genes could be used to develop new target treatments for CC.

scholarly journals Construction of a novel immune gene-based model for prognosis prediction of colorectal cancer

2021 ◽  
Author(s):  
Bin Xie ◽  
jie lin
Keyword(s):  
Gene Expression ◽  
Cox Regression ◽  
Expression Profiles ◽  
Colon Adenocarcinoma ◽  
Gene Set Enrichment Analysis ◽  
Immune Gene ◽  
Prognostic Signature ◽  
Cox Regression Analysis ◽  
Risk Of Death ◽  
Immune Related Genes

Abstract Background Colon adenocarcinoma (COAD) is the third leading cause of cancer-related death. Although surgical treatment and chemotherapy of COAD have made significant progress, its immunotherapy also has great potential, nowadays. Methods Gene expression profiles and clinical data of COAD patients were obtained from The Cancer Genome Atlas_Colon Adenocarcinoma (TCGA_COAD) and Gene Expression Omnibus (GEO) databases, which were further detected for immune-related genes. Immune-related genes were downloaded from Immunology Database and Analysis Portal (ImmPort). LASSO Cox regression analysis was utilized to analyze the immune-related prognostic signature. The prognostic value of the signature was validated by ROC curve. To further detected the potential pathway about immune-related genes in COAD patients, Gene Set Enrichment Analysis (GESA) was used to identify the most significant pathways. Results Finally, a total of 436 immune-related mRNA were identified. Eleven prognosis-related genes were selected to establish an immune-related prognostic signature, which divided patients into high and low risk groups. Several biological processes, such as immune response was enriched. Moreover, our prognosis model has better performance in predicting the 1-, 3-, 5- and 8-years overall survival (OS) for patients from the TCGA and GEO cohort. Also, the complicated signature obtained by combining immune-related signatures with clinical factors provides a more accurate OS predicting compared with individual molecular signatures. Conclusion We have established a prognostic signature consisting of 11 immune-related genes, which may be potential biomarkers for identifying COAD with a high risk of death. Then, the possibility including immunotherapy in personalized COAD treatment was evaluated.

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