scholarly journals CPTAC Pancancer Phosphoproteomics Kinase Enrichment Analysis with ProKAP Provides Insights into Immunogenic Signaling Pathways

2021 ◽  
Author(s):  
Anna Calinawan ◽  
Weiping Ma ◽  
John Erol Evangelista ◽  
Boris Reva ◽  
Francesca Petralia ◽  
...  
Keyword(s):  
Kinase Activity ◽  
Kinase Inhibitors ◽  
Expression Profiles ◽  
Enrichment Analysis ◽  
Proteomics Data ◽  
Multiple Cancer ◽  
Cell Renal Cell Carcinoma ◽  
Tumor Subtypes ◽  
Normal Tissues ◽  
Pan Cancer

The National Cancer Institute (NCI) Clinical Proteomic Tumor Analysis Consortium (CPTAC) initiative has generated extensive phosphoproteomics and proteomics data for tumor and tumoradjacent normal tissue across multiple cancer types. This dataset provides an unprecedented opportunity to systematically characterize pan-cancer kinase activities, which is essential for coupling tumor subtypes with kinase inhibitors as potential treatment. In this work, we performed Kinase Enrichment Analysis (KEA) using a CPTAC phosphoproteomics dataset to identify putative differences in kinase state between tumor and normal tissues within and across five types of cancer. We then implemented an interactive web-portal, the ProTrack Kinase Activity Portal (ProKAP), for querying, visualizing, and downloading the derived pan-cancer kinase activity scores together with the corresponding sample metadata, and protein and phosphoprotein expression profiles. To illustrate the usage of this digital resource, we analyzed the association between kinase activity scores and immune subtypes of clear cell renal cell carcinoma (ccRCC) derived from the CPTAC ccRCC study. We found multiple kinases, whose inhibition has been suggested to have therapeutic effect in other tumor types, are highly active in CD8+-enriched ccRCC tumors. The ProTrack Kinase Activity Portal (ProKAP) is available at: https://pancan-kea3.cptac-data-view.org.

scholarly journals Enolase 1 Correlated With Cancer Progression and Immune-Infiltrating in Multiple Cancer Types: A Pan-Cancer Analysis

2021 ◽  
Vol 10 ◽  
Author(s):  
Wenhua Xu ◽  
Wenna Yang ◽  
Chunfeng Wu ◽  
Xiaocong Ma ◽  
Haoyu Li ◽  
...  
Keyword(s):  
Cancer Progression ◽  
Stress Protein ◽  
Enrichment Analysis ◽  
Tumor Stage ◽  
Gene Set Enrichment Analysis ◽  
The Cancer Genome Atlas ◽  
Multiple Cancer ◽  
Clinical Prognosis ◽  
Multiple Tumor ◽  
Normal Tissues

Enolase 1 (ENO1) is an oxidative stress protein expressed in endothelial cells. This study aimed to investigate the correlation of ENO1 with prognosis, tumor stage, and levels of tumor-infiltrating immune cells in multiple cancers. ENO1 expression and its influence on tumor stage and clinical prognosis were analyzed by UCSC Xena browser, Gene Expression Profiling Interactive Analysis (GEPIA), The Cancer Genome Atlas (TCGA), and GTEx Portal. The ENO1 mutation analysis was performed by cBio Portal, and demonstrated ENO1 mutation (1.8%) did not impact on tumor prognosis. The relationship between ENO1 expression and tumor immunity was analyzed by Tumor Immune Estimation Resource (TIMER) and GEPIA. The potential functions of ENO1 in pathways were investigated by Gene Set Enrichment Analysis. ENO1 expression was significantly different in tumor and corresponding normal tissues. ENO1 expression in multiple tumor tissues correlated with prognosis and stage. ENO1 showed correlation with immune infiltrates including B cells, CD8+ and CD4+ T cells, macrophages, neutrophils, and dendritic cells, and tumor purity. ENO1 was proved to be involved in DNA replication, cell cycle, apoptosis, glycolysis process, and other processes. These findings indicate that ENO1 is a potential prognostic biomarker that correlates with cancer progression immune infiltration.

scholarly journals Development and Interpretation of a Genomic Instability Derived lncRNAs Based Risk Signature as a Predictor of Prognosis for Clear Cell Renal Cell Carcinoma Patients

2021 ◽  
Vol 11 ◽  
Author(s):  
Huiying Yang ◽  
Xiaoling Xiong ◽  
Hua Li
Keyword(s):  
Renal Cell Carcinoma ◽  
High Risk ◽  
Cell Carcinoma ◽  
Genomic Instability ◽  
Renal Cell ◽  
Clear Cell ◽  
Expression Profiles ◽  
Enrichment Analysis ◽  
Cell Renal Cell Carcinoma ◽  
Prognosis Prediction

BackgroundClear cell renal cell carcinoma (ccRCC) is a kind of frequently diagnosed cancer, leading to high death rate in patients. Genomic instability (GI) is regarded as playing indispensable roles in tumorigenesis and impacting the prognosis of patients. The aberrant regulation of long non-coding RNAs (lncRNAs) is a main cause of GI. We combined the somatic mutation profiles and expression profiles to identify GI derived lncRNAs (GID-lncRNAs) in ccRCC and developed a GID-lncRNAs based risk signature for prognosis prediction and medication guidance.MethodsWe decided cases with top 25% cumulative number of somatic mutations as genomically unstable (GU) group and last 25% as genomically stable (GS) group, and identified differentially expressed lncRNAs (GID-lncRNAs) between two groups. Then we developed the risk signature with all overall survival related GID-lncRNAs with least absolute shrinkage and selection operator (LASSO) Cox regression. The functions of the GID-lncRNAs were partly interpreted by enrichment analysis. We finally validated the effectiveness of the risk signature in prognosis prediction and medication guidance.ResultsWe developed a seven-lncRNAs (LINC00460, AL139351.1, AC156455.1, AL035446.1, LINC02471, AC022509.2, and LINC01606) risk signature and divided all samples into high-risk and low-risk groups. Patients in high-risk group were in more severe clinicopathologic status (higher tumor grade, pathological stage, T stage, and more metastasis) and were deemed to have less survival time and lower survival rate. The efficacy of prognosis prediction was validated by receiver operating characteristic analysis. Enrichment analysis revealed that the lncRNAs in the risk signature mainly participate in regulation of cell cycle, DNA replication, material metabolism, and other vital biological processes in the tumorigenesis of ccRCC. Moreover, the risk signature could help assess the possibility of response to precise treatments.ConclusionOur study combined the somatic mutation profiles and the expression profiles of ccRCC for the first time and developed a GID-lncRNAs based risk signature for prognosis predicting and therapeutic scheme deciding. We validated the efficacy of the risk signature and partly interpreted the roles of the seven lncRNAs composing the risk signature in ccRCC. Our study provides novel insights into the roles of genomic instability derived lncRNAs in ccRCC.

scholarly journals Integrative analysis the characterization of peroxiredoxins in pan-cancer

2021 ◽  
Vol 21 (1) ◽  
Author(s):  
Lei Gao ◽  
Jialin Meng ◽  
Chuang Yue ◽  
Xingyu Wu ◽  
Quanxin Su ◽  
...  
Keyword(s):  
Cell Cycle ◽  
Expression Profiles ◽  
Genetic Alterations ◽  
Molecular Characteristics ◽  
Loss Of Function ◽  
Systematic Analysis ◽  
Normal Tissues ◽  
Bladder Cell ◽  
Cox Proportional Hazard Regression ◽  
Pan Cancer

Abstract Background Peroxiredoxins (PRDXs) are an antioxidant enzymes protein family involved in several biological functions such as differentiation, cell growth. In addition, previous studies report that PRDXs play critical roles in the occurrence and development of carcinomas. However, few studies have conducted systematic analysis of PRDXs in cancers. Therefore, the present study sought to explore the molecular characteristics and potential clinical significance of PRDX family members in pan cancer and further validate the function of PRDX6 in bladder urothelial carcinoma (BLCA). Methods A comprehensive analysis of PRDXs in 33 types of cancer was performed based on the TCGA database. This involved an analysis of mRNA expression profiles, genetic alterations, methylation, prognostic values, potential biological pathways and target drugs. Moreover, both the gain and loss of function strategies were used to assess the importance and mechanism of PRDX6 in the cell cycle of BLCA. Result Analysis showed abnormal expression of PRDX1-6 in several types of cancer compared to normal tissues. Univariate Cox proportional hazard regression analysis showed that expression levels of PRDX1, PRDX4 and PRDX6 were mostly associated with poor survival of OS, DSS and PFI, and PRDX2 and PRDX3 with favorable survival. In addition, the expression of PRDX genes were positively correlated with CNV and negatively with methylation. Moreover, analysis based on PharmacoDB dataset showed that the augmented levels of PRDX1, PRDX3 and PRDX6 were significantly correlated with EGFR/VEGFR inhibitor drugs. Furthermore, knocking down of PRDX6 inhibited growth of cancer cells through the JAK2-STAT3 in bladder cell lines. Conclusions PRDXs are potential biomarkers and therapeutic targets for several carcinomas, especially for BLCA. In addition, PRDX6 could regulate proliferation of cancer cell via JAK2-STAT3 pathway and involve into the process of cell cycle in BLCA.

scholarly journals Identification of Prognostic Biomarkers of Potential Hub Genes in Urothelial Carcinoma and Function in Microenvironment

2021 ◽  
Author(s):  
Wei Chu ◽  
Bing Zhang ◽  
Haifeng Gong ◽  
Qianqian Zhao ◽  
Jun Chen ◽  
...  
Keyword(s):  
T Cells ◽  
Urothelial Carcinoma ◽  
Checkpoint Inhibitors ◽  
Expression Profiles ◽  
Gene Expression Profiles ◽  
Enrichment Analysis ◽  
Receptor Interaction ◽  
Hub Genes ◽  
Database Analysis ◽  
Normal Tissues

Abstract Background: Urothelial carcinoma (UC) is the most common histological type of urinary system. In the past decades, despite the advances in UC diagnosis and therapy, there are still challenges to improve the overall survival (OS) of UC patients. PD-L1 inhibitor and PD-1 inhibitor have been approved for treating invasive UC, however, only about 20% of patients with metastatic UC show clinical benefits from immune checkpoint inhibitors. Therefor, bioinformatics tools were utilized to screen prognostic-related biomarkers, and analyze their relationship with immunocyte in UC, hoping to provide new ideas for the clinical treatment of UC patients.Methods: Three gene expression profiles (i.e. GSE32548, GSE32894 and GSE48075) were selected from GEO, and divide them into invasive and superficial UC group for study. NetworkAnalyst tool was used to construct gene regulatory network of DEGs, while DAVID and Metascape were utilized to perform GO/KEGG enrichment analysis of DEGs. The hub genes were screened by STRING and cytoscape, and the ONCOMINE, GEPIA, UALCAN, cBioPortal and HPA databases were used to analyze the expression differences at the DNA, RNA, protein levels and prognostic of UC. TIMER was used to analyze the relationship between hub genes and immunocyte infiltration.Results: In total, 63 DEGs were identified from the GEO database of UC, of which 31 and 32 were up-and down-regulated. GO/KEGG pathway analysis identified DEGs were mainly enriched in the collagen catabolic process, extracellular matrix (ECM) organization, ECM structural constituent and ECM-receptor interaction. Nine hub genes (i.e. COL1A1, COL1A2, COL3A1, COL5A2, MMP9, POSTN, SPP1, VCAN and THBS2) upregulated in invasive UC compared with superficial UC were identified. cBioportal database analysis showed that 35% of UC patients presented genetic variants in the hub genes, of which amplification and deletion mutations were the most common. ONCOMINE and UALCAN database analysis showed that the mRNA expression of all hub genes in invasive UC was significantly higher than that in superficial UC and normal tissues. HPA database analysis showed that there was up-regulation of COL3A1, SPP1, POSTN and VCAN protein in UC tissues than in normal tissues. GEPIA showed that COL1A2, COL3A1, THBS2, and VCAN were positively correlated with the OS rate among patients with UC (P < 0.05). UALCAN showed that UC patients with high expression of COL1A1, COL1A2, COL5A2 and POSTN had a poorer prognosis (P < 0.05). TRRUST database analysis indicated that there was a significant correlation between the expression of the hub genes and the infiltration of CD4+T cells, CD8+T cells, macrophages, neutrophils and dendritic cells. Conclusion: Hub genes played important roles in pathogenesis and treatment prognosis of UC and they can provides new biomolecular predictions for immunotherapy and prognosis judgment of UC.

scholarly journals Identification of Prognostic Biomarkers of Potential Hub Genes in Urothelial Carcinoma and Function in Microenvironment

2021 ◽  
Author(s):  
Wei Chu ◽  
Bing Zhang ◽  
Haifeng Gong ◽  
Qianqian Zhao ◽  
Jun Chen ◽  
...  
Keyword(s):  
T Cells ◽  
Urothelial Carcinoma ◽  
Checkpoint Inhibitors ◽  
Expression Profiles ◽  
Enrichment Analysis ◽  
Receptor Interaction ◽  
Hub Genes ◽  
Database Analysis ◽  
Protein Levels ◽  
Normal Tissues

Abstract Background Urothelial carcinoma (UC) is the most common histological type of urinary system. In the past decades, despite the advances in UC diagnosis and therapy, there are still challenges to improve the overall survival (OS) of UC patients. PD-L1 inhibitor and PD-1 inhibitor have been approved for treating invasive UC, however, only about 20% of patients with metastatic UC show clinical benefits from immune checkpoint inhibitors. Therefor, bioinformatics tools were utilized to screen prognostic-related biomarkers, and analyze their relationship with immunocyte in UC, hoping to provide new ideas for the clinical treatment of UC patients.Methods Three gene expression profiles (i.e. GSE32548, GSE32894 and GSE48075) were selected from GEO, and divide them into invasive and superficial UC group for study. NetworkAnalyst tool was used to construct gene regulatory network of DEGs, while DAVID and Metascape were utilized to perform gene ontology (GO) and kyoto encyclopedia of genes and genomes (KEGG) enrichment analysis of DEGs. The hub genes were screened by STRING and cytoscape, and the ONCOMINE, GEPIA, UALCAN, cBioPortal and HPA databases were used to analyze the expression differences and survival curves of UC at the DNA, RNA, protein levels and protein levels. TIMER was used to analyze the relationship between hub genes and immunocyte infiltration.Results In total, 63 DEGs were identified from the GEO database of UC, of which 31 and 32 were up-and down-regulated. GO/KEGG pathway analysis identified DEGs were mainly enriched in the collagen catabolic process, extracellular matrix (ECM) organization, ECM structural constituent and ECM-receptor interaction. Nine hub genes (i.e. COL1A1, COL1A2, COL3A1, COL5A2, MMP9, POSTN, SPP1, VCAN and THBS2) upregulated in invasive UC compared with superficial UC were identified. cBioportal database analysis showed that 35% of UC patients presented genetic variants in the hub genes, of which amplification and deletion mutations were the most common. ONCOMINE and UALCAN database analysis showed that the mRNA expression of all hub genes in invasive UC was significantly higher than that in superficial UC and normal tissues. HPA database analysis showed that there was up-regulation of COL3A1, SPP1, POSTN and VCAN protein in UC tissues than in normal tissues. GEPIA showed that COL1A2, COL3A1, THBS2, and VCAN were positively correlated with the OS rate among patients with UC (P < 0.05). UALCAN showed that UC patients with high expression of COL1A1, COL1A2, COL5A2 and POSTN had a poorer prognosis (P < 0.05). TRRUST database analysis indicated that there was a significant correlation between the expression of the hub genes and the infiltration of CD4 + T cells, CD8 + T cells, macrophages, neutrophils and dendritic cells.Conclusion Hub genes played important roles in pathogenesis and treatment prognosis of UC. Hub genes analysis provides new predictive biomolecules for UC immunotherapy and prognosis judgment.

scholarly journals A pan-cancer analysis of the glutaminase and their association with prognosis, tumor microenvironment, and therapeutic targets

2021 ◽  
Author(s):  
Sheng-Jie Jin ◽  
Lian-bao Kong
Keyword(s):  
Drug Sensitivity ◽  
Cancer Metabolism ◽  
Enrichment Analysis ◽  
Gene Set Enrichment Analysis ◽  
Glutamine Metabolism ◽  
Gene Set Enrichment ◽  
Normal Tissues ◽  
Checkpoint Genes ◽  
Pan Cancer ◽  
Cancer Bioinformatics

Abstract BackgroundGlutamine metabolism plays a key role in various biological processes of tumor. Glutaminase (GLS) is involved in Glutamine metabolism and plays a conserved regulatory role in the process. Nevertheless, there is no comprehensively analysis of GLS in pan-cancer.MaterialsComprehensive bioinformatics analysis was adopted to investigate the expression level, prognostic values, and association between expression of GLS and TME, immune cells' infiltration, immune checkpoint genes, TMB, MSI, drug sensitivity in pan-cancer. Bioinformatics tools including CCLE, immunophenoscore (IPS), Tumor Immune Dysfunction and Exclusion (TIDE), GSEA, and TIMER databases were used.ResultsDifferently expressed GLS between tumor and normal tissues were analyzed, and the clinical prognoses, MMR, MSI, and TMB in multiple types of cancer were associated with GLS expression. Furthermore, GLS closely correlated with tumor immunity and drug sensitivity, and found GLS were predicted to be involved in cancer metabolism and immunity pathways, through gene set enrichment analysis (GSEA).ConclusionThe GLS expression could be used as a prognostic biomarker for determining prognosis and provide further insights into anti-glutamine metabolism for cancer.

scholarly journals Pan-cancer characterization of GDF15 as a potential prognosis and immunological biomarker

2021 ◽  
Author(s):  
Xiaolei Li ◽  
Qian Huai ◽  
Cheng Zhu ◽  
Yishan Du ◽  
Fang Ma ◽  
...  
Keyword(s):  
Cell Lines ◽  
Cellular Proliferation ◽  
Expression Profiles ◽  
Enrichment Analysis ◽  
Genetic Alterations ◽  
Loss Of Function ◽  
Comprehensive Overview ◽  
Immune Infiltration ◽  
Functional Mechanisms ◽  
Pan Cancer

Abstract Background Growth/differentiation factor 15 (GDF15) is a member of the TGF-b superfamily whose expression is increased in response to cellular stress and disease. Although emerging cell-or animal-based evidence supports the relationship between GDF15 and cancers, systematic pan-cancer analysis of GDF15 remains unavailable. Thus, we aimed to explore the prognostic and immunological roles of GDF15 across different types of tumors. Methods A comprehensive analysis of GDF15 in 33 types of cancer was performed based on the TCGA database. This involved an analysis of mRNA expression profiles, genetic alterations, methylation, prognostic values, immune infiltration analysis, potential biological pathways. Moreover, both the gain and loss of function strategies were used to assess the function of GDF15 in cell lines of hepatocellular carcinoma (HCC). Results GDF15 is highly expressed in most types of cancers, and there is a significant correlation between the expression of GDF15 and the prognosis of cancer patients. We have observed that GDF15 promotes the proliferation and invasion of HCC cell lines. Subsequently, methylation analyses suggested that high GDF15 expression likely resulted from hypomethylation, and immune infiltration analysis showed that GDF15 may have an impact on the changes in the tumor microenvironment. Moreover, enrichment analysis revealed that TGF-beta signaling and metabolism pathways were involved in the functional mechanisms of GDF15. Conclusions Our unpresented pan-cancer analysis of GDF15 offers a relatively comprehensive overview of the oncogenic roles of GDF15 in multiple human cancers. GDF15 may prompt HCC cellular proliferation, invasion and metastasis. All of these provides solid basement and will promote more advanced understanding the role of GDF15 in tumorigenesis and development from the perspective of clinical tumor samples and cells.

scholarly journals Comprehensive functional profiling of long non-coding RNAs through a novel pan-cancer integration approach and modular analysis of their protein-coding gene association networks

2018 ◽  
Author(s):  
Kevin Walters ◽  
Radmir Sarsenov ◽  
Wen Siong Too ◽  
Roseanna K. Hare ◽  
Ian C. Paterson ◽  
...  
Keyword(s):  
Genomic Sequence ◽  
Therapeutic Potential ◽  
Tumour Progression ◽  
Expression Profiles ◽  
Critical Role ◽  
Multiple Cancer ◽  
Protein Coding ◽  
Cancer Types ◽  
Non Coding Rnas ◽  
Pan Cancer

AbstractLong non-coding RNAs (lncRNAs) are emerging as crucial regulators of cellular processes in diseases such as cancer, although the functions of most remain poorly understood. To address this, here we apply a novel strategy to integrate gene expression profiles across 32 cancer types, and cluster human lncRNAs based on their pan-cancer protein-coding gene associations. By doing so, we derive 16 lncRNA modules whose unique properties allow simultaneous inference of function, disease specificity and regulation for over 800 lncRNAs. Remarkably, modules could be grouped into just four functional themes: transcription regulation, immunological, extracellular, and neurological, with module generation frequently driven by lncRNA tissue specificity. Notably, three modules associated with the extracellular matrix represented potential networks of lncRNAs regulating key events in tumour progression. These included a tumour-specific signature of 33 lncRNAs that may play a role in inducing epithelialmesenchymal transition through modulation of TGFβ signalling, and two stromal-specific modules comprising 26 lncRNAs linked to a tumour suppressive microenvironment, and 12 lncRNAs related to cancer-associated fibroblasts. At least one member of the 12-lncRNA signature was experimentally supported by siRNA knockdown, which resulted in attenuated differentiation of quiescent fibroblasts to a cancer-associated phenotype. Overall, the study provides a unique pan-cancer perspective on the lncRNA functional landscape, acting as a global source of novel hypotheses on lncRNA contribution to tumour progression.Author SummaryThe established view of protein production is that genomic DNA is transcribed into RNA, which is then translated into protein. Proteins play a critical role in shaping the function of each individual cell in the human body yet they represent less than 2% of human genomic sequence whilst up to 90% of the genome is transcribed. To explain this disparity, the existence of thousands of long non-coding RNAs (lncRNAs) has emerged that do not encode proteins but perform function as an RNA molecule. Most lncRNAs have yet to be assigned a specific biological role, so to address this we apply a novel computational approach to characterise the function of >800 lncRNAs through consistent association with protein coding genes across multiple cancer types. By doing so, we discover 16 “modules” of closely related lncRNAs that share broad functional themes, the most compelling of which consists of 12 lncRNAs that could regulate activation of specific cells neighbouring the tumour, leading to accelerated tumour progression and invasion. Overall, the study provides the most robust view of the lncRNA-protein coding gene landscape to date, adding to growing evidence that lncRNAs are key regulators of cancer, and have therapeutic potential comparable to proteins.

scholarly journals Systematic Profiling of Invasion-related Genes Signature Predicts Prognostic Features and Identificates Molecular Subtypes of Lung Adenocarcinoma

2021 ◽  
Author(s):  
Ping Yu ◽  
Linlin Tong ◽  
Yujia Song ◽  
Hui Qu ◽  
Ying Chen
Keyword(s):  
Gene Expression ◽  
Lung Adenocarcinoma ◽  
Molecular Subtypes ◽  
Expression Profiles ◽  
Enrichment Analysis ◽  
Gene Signature ◽  
Gene Set Enrichment Analysis ◽  
Normal Tissues ◽  
Tumor Tissues ◽  
Cox Analysis

Abstract Background: Due to the high heterogeneity of lung adenocarcinoma (LUAD), molecular subtype based on gene expression profiles is of great significance for diagnosis, and prognosis prediction in patients with LUAD.Methods: Invasion-related genes were obtained from the CancerSEA database, and LUAD expression profiles were downloaded from The Cancer Genome Atlas. The ConsensusClusterPlus was used to obtain molecular subtypes based on invasion-related genes. The limma software package was used to identify differentially expressed genes (DEGs). A multi-gene risk model was constructed by Lasso-Cox analysis. A nomogram was also constructed based on risk scores and meaningful clinical features.Results: 3 subtypes (C1, C2, C3) based on the expression of invasion-related genes were obtained. C3 had the worst prognosis. A total of 669 DEGs were identified among the subtypes. Pathway enrichment analysis results showed that the DEGs were mainly enriched in the cell cycle, DNA replication, the p53 signaling pathway, and other tumor-related pathways. A 5-gene signature (KRT6A, MELTF, IRX5, MS4A1, CRTAC1) was identified by using Lasso-Cox analysis. The training, validation, and external independent cohorts proved that the model was robust and had better prediction ability than other lung cancer models. The gene expression results showed that the expression levels of MS4A1 and KRT6A in tumor tissues were higher than in normal tissues, while CRTAC1 expression in tumor tissues was lower than in normal tissues. At the same time, the 5 genes were significantly expressed in pan-cancer immune subtypes. Gene set enrichment analysis showed that MS4A1, KRT6A, and CRAT1 genes were both enriched in the HALLMARK_IL2_STAT5_SIGNALING pathway, and IRX5 and MELTF gene were both enriched in the HALLMARK_EPITHELIAL_MESENCHYMAL_TRANSITION pathway. Conclusion: The 5-gene signature prognostic stratification system based on invasion-related genes could be used to assess prognostic risk in patients with LUAD.

scholarly journals Zic Family Member 2 (ZIC2): a Potential Diagnostic and Prognostic Biomarker for Pan-Cancer

2021 ◽  
Vol 8 ◽  
Author(s):  
Zhengtong Lv ◽  
Lin Qi ◽  
Xiheng Hu ◽  
Miao Mo ◽  
Huichuan Jiang ◽  
...  
Keyword(s):  
Tumor Microenvironment ◽  
Zinc Finger Protein ◽  
Enrichment Analysis ◽  
Gene Set Enrichment Analysis ◽  
The Cancer Genome Atlas ◽  
Multiple Cancer ◽  
Survival Prognosis ◽  
Multiple Tumor ◽  
Immune Related Genes ◽  
Pan Cancer

Background: As a transcription factor, Zinc finger protein ZIC2 can interact with various DNAs and proteins. Current studies have shown that ZIC2 plays an oncogene role in various cancers. In this study, we systematically characterize the prevalence and predictive value of ZIC2 expression across multiple cancer types.Methods: We mined several public databases, including Oncomine, the Cancer Genome Atlas (TCGA), cBioPortal, Kaplan-Meier Plotter and PrognoScan to evaluated the differentially expressed ZIC2 between tumor samples and normal control samples in pan-cancner, and then explored the association between ZIC2 expression and patient survival, prognosis and clinicopathologic stage. We also analyzed the relationship between tumor mutation burden (TMB), microsatellite instability (MSI), tumor microenvironment, tumor- and immune-related genes and ZIC2 expression. Finally, we explored the potential signaling pathway mechanism through gene set enrichment analysis (GSEA).Results: ZIC2 expression was higher in most cancer tissues compared with adjacent normal tissues. High ZIC2 expression was associated with worse prognosis and a higher clinicopathologic stage. ZIC2 expression was strongly associated with the TMB, MSI, tumor microenvironment and tumor- and immune-related genes. The GSEA revealed that multiple tumor- and immune-related pathways were differentially enriched in ZIC2 high or low expression phenotype.Conclusion: ZIC2 expression may be a potential prognostic molecular biomarker of poor survival in pan-cancer and may act as an oncogene with a strong effect in the processes of tumorigenesis and progression.

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