Immune-Related Gene SERPINE1 Is a Novel Biomarker for Diffuse Lower-Grade Gliomas via Large-Scale Analysis

BackgroundGlioma is one of the highly fatal primary tumors in the central nervous system. As a major component of tumor microenvironment (TME), immune cell has been proved to play a critical role in the progression and prognosis of the diffuse lower-grade gliomas (LGGs). This study aims to screen the key immune-related factors of LGGs by investigating the TCGA database.MethodsThe RNA-sequencing data of 508 LGG patients were downloaded in the TCGA database. ESTIMATE algorithm was utilized to calculate the stromal, immune, and ESTIMATE scores, based on which, the differentially expressed genes (DEGs) were analyzed by using “limma” package. Cox regression analysis and the cytoHubba plugin of Cytoscape software were subsequently applied to screen the survival-related genes and hub genes, the intersection of which led to the identification of SERPINE1 that played key roles in the LGGs. The expression patterns, clinical features, and regulatory mechanisms of SERPINE1 in the LGGs were further analyzed by data mining of the TCGA database. What’s more, the above analyses of SERPINE1 were further validated in the LGG cohort from the CGGA database.ResultWe found that stromal and immune cell infiltrations were strongly related to the prognosis and malignancy of the LGGs. A total of 54 survival-related genes and 46 hub genes were screened out in the DEGs, within which SERPINE1 was identified to be significantly overexpressed in the LGG samples compared with the normal tissues. Moreover, the upregulation of SERPINE1 was more pronounced in the gliomas of WHO grade III and IDH wild type, and its expression was correlated with poor prognosis in the LGG patients. The independent prognostic value of SERPINE1 in the LGG patients was also confirmed by Cox regression analysis. In terms of the functions of SERPINE1, the results of enrichment analysis indicated that SERPINE1 was mainly enriched in the immune‐related biological processes and signaling pathways. Furthermore, it was closely associated with infiltrations of immune cells in the LGG microenvironment and acted synergistically with PD1, PD-L1, PD-L2.ConclusionThese findings proved that SERPINE1 could serve as a prognostic biomarker and potential immunotherapy target of LGGs.

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Low Expression of RILPL2 Predicts Poor Prognosis and Correlates With Immune Infiltration in Endometrial Carcinoma

Frontiers in Molecular Biosciences ◽

10.3389/fmolb.2021.670893 ◽

2021 ◽

Vol 8 ◽

Author(s):

Jinhui Liu ◽

Mengting Xu ◽

Zhipeng Wu ◽

Yan Yang ◽

Shuning Yuan ◽

...

Keyword(s):

Regression Analysis ◽

Endometrial Carcinoma ◽

Immune Cell ◽

Cox Regression ◽

Critical Role ◽

Enrichment Analysis ◽

The Cancer Genome Atlas ◽

Cox Regression Analysis ◽

Kaplan Meier ◽

Promising Target

Increasing numbers of biomarkers have been identified in various cancers. However, biomarkers associated with endometrial carcinoma (EC) remain largely to be explored. In the current research, we downloaded the RNA-seq data and corresponding clinicopathological features from the Cancer Genome Atlas (TCGA) database. We conducted an expression analysis, which resulted in RILPL2 as a novel diagnostic biomarker in EC. The dysregulation of RILPL2 in EC was also validated in multiple datasets. The correlations between clinical features and RILPL2 expression were assessed by logistic regression analysis. Then, Kaplan-Meier analysis, univariate and multivariate Cox regression analysis were performed to estimate prognostic values of RILPL2 in the TCGA cohort, which revealed that increased level of RILPL2 was remarkably associated with better prognosis and could act as an independent prognostic biomarker in patients with EC. Moreover, correlation analysis of RILPL2 and tumor-infiltrating immune cells (TIICs) indicated that RILPL2 might play a critical role in regulating immune cell infiltration in EC and is related to immune response. Besides, high methylation level was a significant cause of low RILPL2 expression in EC. Subsequently, weighted gene co-expression network analysis (WGCNA) and enrichment analysis were conducted to explore the RILPL2-involved underlying oncogenic mechanisms, and the results indicated that RILPL2 mainly regulated cell cycle. In conclusion, our findings provided evidence that downregulation of RILPL2 in EC is an indicator of adverse prognosis and RILPL2 may act as a promising target for the therapeutics of EC.

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Low Expression of RILPL2 Predicts Poor Prognosis and Correlates with Immune Infiltration in Endometrial Carcinoma

10.21203/rs.3.rs-390545/v1 ◽

2021 ◽

Author(s):

Jinihui Liu ◽

Xu Mengtimg ◽

Zhipemg Wu ◽

Jianqiamg Liamg ◽

Hongjun Zhu

Keyword(s):

Regression Analysis ◽

Endometrial Carcinoma ◽

Immune Cell ◽

Cox Regression ◽

Critical Role ◽

Enrichment Analysis ◽

The Cancer Genome Atlas ◽

Cox Regression Analysis ◽

Kaplan Meier ◽

Low Expression

Abstract Increasing numbers of biomarkers have been identified for various cancers. However, biomarkers associated with endometrial carcinoma (EC) remain largely to be explored. In the current research, we downloaded the RNA-seq data and corresponding clinicopathological features from the Cancer Genome Atlas (TCGA) database. We conducted expression analysis, which resulted in identification of RILPL2 as a novel diagnostic biomarker in EC. The dysregulation of RILPL2 in EC was also validated in multiple datasets. The correlations between clinical features and RILPL2 expression were assessed by logistic regression analysis. Then, Kaplan-Meier analysis, univariate, and multivariate Cox regression analysis were performed to estimate prgnostic values of RILPL2 in the TCGA cohort, which unveiled that increased level of RILPL2 was remarkably associated with better prognosis and could be severd as an independent prognostic biomarker in patients with EC. Moreover, correlation analysis of RILPL2 and tumor-infiltrating immune cells (TIICs) indicated that RILPL2 might play a critical role in regulating immune cell infiltration in EC and is related to immune response. Besides, high methylation level was a significant cause for RILPL2 low expression in EC. Subsequently, weighted gene co-expression network analysis (WGCNA) and enrichment analysis were conducted to explore the RILPL2-involved underlyingl oncogenic mechanisms, and the results indicated that RILPL2 mainly regulated cell cycle. In conclusion, our findings provided evidence that downregulation of RILPL2 in EC is an indicator of adverse prognosis and RILPL2 may act as a promising target for the theraputics of EC.

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A novel ferroptosis related gene signature is associated with prognosis in patients with ovarian serous cystadenocarcinoma

Scientific Reports ◽

10.1038/s41598-021-90126-5 ◽

2021 ◽

Vol 11 (1) ◽

Author(s):

Zhixiang Yu ◽

Haiyan He ◽

Yanan Chen ◽

Qiuhe Ji ◽

Min Sun

Keyword(s):

Cox Regression ◽

Expression Profiles ◽

Critical Role ◽

Gene Signature ◽

Cancer Genome ◽

Training Data ◽

Hub Genes ◽

Validation Data ◽

Data Set ◽

Cox Regression Analysis

AbstractOvarian cancer (OV) is a common type of carcinoma in females. Many studies have reported that ferroptosis is associated with the prognosis of OV patients. However, the mechanism by which this occurs is not well understood. We utilized Genotype-Tissue Expression (GTEx) and The Cancer Genome Atlas (TCGA) to identify ferroptosis-related genes in OV. In the present study, we applied Cox regression analysis to select hub genes and used the least absolute shrinkage and selection operator to construct a prognosis prediction model with mRNA expression profiles and clinical data from TCGA. A series of analyses for this signature was performed in TCGA. We then verified the identified signature using International Cancer Genome Consortium (ICGC) data. After a series of analyses, we identified six hub genes (DNAJB6, RB1, VIMP/ SELENOS, STEAP3, BACH1, and ALOX12) that were then used to construct a model using a training data set. The model was then tested using a validation data set and was found to have high sensitivity and specificity. The identified ferroptosis-related hub genes might play a critical role in the mechanism of OV development. The gene signature we identified may be useful for future clinical applications.

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Identification and Analysis of Three Hub Prognostic Genes Related to Osteosarcoma Metastasis

Journal of Oncology ◽

10.1155/2021/6646459 ◽

2021 ◽

Vol 2021 ◽

pp. 1-16

Author(s):

Jianye Tan ◽

Haofeng Liang ◽

Bingsheng Yang ◽

Shuang Zhu ◽

Guofeng Wu ◽

...

Keyword(s):

Regression Analysis ◽

Cox Regression ◽

Survival Rates ◽

Prognostic Biomarkers ◽

Messenger Rnas ◽

Hub Genes ◽

Cox Regression Analysis ◽

Expression Levels ◽

Kaplan Meier ◽

Before And After

Osteosarcoma (OS) often occurs in children and often undergoes metastasis, resulting in lower survival rates. Information on the complexity and pathogenic mechanism of OS is limited, and thus, the development of treatments involving alternative molecular and genetic targets is hampered. We categorized transcriptome data into metastasis and nonmetastasis groups, and 400 differential RNAs (230 messenger RNAs (mRNAs) and 170 long noncoding RNAs (lncRNAs)) were obtained by the edgeR package. Prognostic genes were identified by performing univariate Cox regression analysis and the Kaplan–Meier (KM) survival analysis. We then examined the correlation between the expression level of prognostic lncRNAs and mRNAs. Furthermore, microRNAs (miRNAs) corresponding to the coexpression of lncRNA-mRNA was predicted, which was used to construct a competitive endogenous RNA (ceRNA) regulatory network. Finally, multivariate Cox proportional risk regression analysis was used to identify hub prognostic genes. Three hub prognostic genes (ABCG8, LOXL4, and PDE1B) were identified as potential prognostic biomarkers and therapeutic targets for OS. Furthermore, transcriptions factors (TFs) (DBP, ESX1, FOS, FOXI1, MEF2C, NFE2, and OTX2) and lncRNAs (RP11-357H14.16, RP11-284N8.3, and RP11-629G13.1) that were able to affect the expression levels of genes before and after transcription were found to regulate the prognostic hub genes. In addition, we identified drugs related to the prognostic hub genes, which may have potential clinical applications. Immunohistochemistry (IHC) and quantitative real-time polymerase chain reaction (qRT-PCR) confirmed that the expression levels of ABCG8, LOXL4, and PDE1B coincided with the results of bioinformatics analysis. Moreover, the relationship between the hub prognostic gene expression and patient prognosis was also validated. Our study elucidated the roles of three novel prognostic biomarkers in the pathogenesis of OS as well as presenting a potential clinical treatment for OS.

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Prognostic Significance and Gene Co-Expression Network of PLAU and PLAUR in Gliomas

Frontiers in Oncology ◽

10.3389/fonc.2021.602321 ◽

2022 ◽

Vol 11 ◽

Author(s):

Junhong Li ◽

Huanhuan Fan ◽

Xingwang Zhou ◽

Yufan Xiang ◽

Yanhui Liu

Keyword(s):

Cox Regression ◽

Prognostic Significance ◽

High Expression ◽

Lower Grade ◽

Tumor Type ◽

Who Grade ◽

Cox Regression Analysis ◽

Online Databases ◽

Type Plasminogen Activator ◽

Primary Glioma

The urokinase-type plasminogen activator(PLAU) and its receptor PLAUR participate in a series of cell physiological activities on the extracellular surface. Abnormal expression of PLAU and PLAUR is associated with tumorigenesis. This study aims to evaluate the prognostic value of PLAU/PLAUR transcription expression in glioma and to explore how they affect the generation and progression of glioma. In this study, online databases are applied, such as Oncomine, GEPIA, CGGA, cBioPortal, and LinkedOmics. Overexpression of PLAU/PLAUR was found to be significantly associated with clinical variables including age, tumor type, WHO grade, histology, IDH-1 mutation, and 1p19q status. PLAU and PLAUR had a high correlation in transcriptional expression levels. High expression of PLAU and PLAUR predicted a poor prognosis in primary glioma and recurrent glioma patients, especially in lower grade gliomas. Cox regression analysis indicated that high expression of PLAU and PLAUR were independent prognostic factors for shorter overall survival in glioma patients. In gene co-expression network analysis PLAU and PLAUR and their co-expression genes were found to be involved in inflammatory activities and tumor-related signaling pathways. In conclusion, PLAU and PLAUR could be promising prognostic biomarkers and potential therapeutic targets of glioma patients.

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An Immune-Related Prognostic Signature for Predicting Clinical Outcomes and Immune Landscape in IDH-Mutant Lower-Grade Gliomas

Journal of Oncology ◽

10.1155/2021/3766685 ◽

2021 ◽

Vol 2021 ◽

pp. 1-19

Author(s):

Gang Xiao ◽

Xuan Gao ◽

Lifeng Li ◽

Chao Liu ◽

Zhiyuan Liu ◽

...

Keyword(s):

High Risk ◽

Clinical Outcomes ◽

Immune Cell ◽

Cox Regression ◽

Immune Escape ◽

Gene Signature ◽

Individual Therapy ◽

Lower Grade ◽

Prognostic Signature ◽

Cox Regression Analysis

Background. IDH mutation is the most common in diffuse LGGs, correlated with a favorable prognosis. However, the IDH-mutant LGGs patients with poor prognoses need to be identified, and the potential mechanism leading to a worse outcome and treatment options needs to be investigated. Methods. A six-gene immune-related prognostic signature in IDH-mutant LGGs was constructed based on two public datasets and univariate, multivariate, and LASSO Cox regression analysis. Patients were divided into low- and high-risk groups based on the median risk score in the training and validation sets. We analyzed enriched pathways and immune cell infiltration, applying the GSEA and the immune evaluation algorithms. Results. Stratification and multivariate Cox analysis unveiled that the six-gene signature was an independent prognostic factor. The signature (0.806/0.795/0.822) showed a remarkable prognostic performance, with 1-, 3-, and 5-year time-dependent AUC, higher than for grade (0.612/0.638/0.649) and 1p19q codeletion status (0.606/0.658/0.676). High-risk patients had higher infiltrating immune cells. However, the specific immune escape was observed in the high-risk group after immune activation, owing to increasing immunosuppressive cells, inhibitory cytokines, and immune checkpoint molecules. Moreover, a novel nomogram model was developed to evaluate the survival in IDH-mutant LGGs patients. Conclusion. The six-gene signature could be a promising prognostic biomarker, which is promising to promote individual therapy and improve the clinical outcomes of IDH-mutant gliomas. The study also refined the current classification system of IDH-mutant gliomas, classifying patients into two subtypes with distinct immunophenotypes and overall survival.

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The association between plasma furin and cardiovascular events after acute myocardial infarction

10.21203/rs.3.rs-41696/v2 ◽

2020 ◽

Author(s):

Zhi-wei Liu ◽

Qiang Ma ◽

Jie Liu ◽

Jing-Wei Li ◽

Yun-Dai Chen

Keyword(s):

Myocardial Infarction ◽

Acute Myocardial Infarction ◽

Regression Analysis ◽

Cox Regression ◽

Critical Role ◽

Cardiac Events ◽

Cox Regression Analysis ◽

Kaplan Meier ◽

Atheromatous Plaques ◽

Key Enzyme

Abstract Background: Furin is the key enzyme to cleave pro-BNP and plays a critical role in the cardiovascular system through its involvement in the lipid metabolism, blood pressure and formation of atheromatous plaques. NT-proBNP and recently corin, which is also a key enzyme to cleave pro-BNP, have been approved as predictors of prognosis after acute myocardial infarction (AMI). We here conducted this cohort study to investigate the relationship between plasma furin and the prognosis outcome in patients after AMI. Methods: We enrolled 1100 AMI patients and measured their plasma furin concentration. The primary endpoint was the major adverse cardiac events (MACE), a composite of cardiovascular (CV) death, non-fatal myocardial infarction or non-fatal stroke. The association of plasma furin concentration with AMI outcomes was explored by using Kaplan–Meier curve and multivariate Cox regression analysis. Results: Our results showed that slight increase of mean cTNT in patients with higher furin concentration (P=0.016). Over a median follow-up of 31 months, multivariate Cox regression analysis suggested that plasma furin was not associated with MACE (HR: 1.01; 95% CI: 0.93-1.06; P=0.807) after adjustment for potential conventional risk factors. However, plasma furin was associated with non-fatal MI (HR: 1.09; 95% CI: 1.01-1.17; P=0.022) after fully adjustment. Subgroup analysis indicated no relationship between plasma furin and MACE in different subgroup populations.Conclusions: Our study demonstrated that plasma furin was not associated with risk of MACE and may not be used as a predictor of poor prognosis after AMI. But higher levels of plasma furin may be associated with higher risk of non-fatal MI.

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

Frontiers in Genetics ◽

10.3389/fgene.2021.687979 ◽

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.

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Gene Expression Analysis Reveals Novel Gene Prognostic Signatures Involved in Medulloblastoma Subgroups

10.21203/rs.3.rs-328561/v1 ◽

2021 ◽

Author(s):

Fangcheng Li ◽

Hong-Yao Yuan ◽

Cheng Chen ◽

Jun-Ping Pan ◽

Xin-Ke Xu ◽

...

Keyword(s):

Regression Analysis ◽

Gene Expression Analysis ◽

Cox Regression ◽

Molecular Subtypes ◽

Molecular Subtype ◽

Specific Treatment ◽

Single Factor ◽

Hub Genes ◽

Cox Regression Analysis ◽

Pathway Enrichment

Abstract PurposeMedulloblastoma is a malignant childhood tumor with four molecular subtypes: WNT, SHH, G3, and G4. The prognosis of these four molecular subtypes is different. WNT has the best prognosis, followed by SHH, and G3 and G4 subtypes have the worst prognosis. This study aimed to identify various molecular subtypes of medulloblastoma can independently predict the prognosis of patients and provide specific treatment way for them.MethodsBased on the data in the GSE37418 dataset, the WGCNA method was used to find the genes most related to these molecular subtypes, and then the top ten hub genes in each subtype were found through the cytohubba plug-in of cytoscape. GO pathway enrichment of four interested modules was used, and then GSE85217 was used for clinical trials in single-factor Cox regression analysis .ResultsThe information was subjected to single-factor Cox regression analysis, and twelve hub genes with the most significant prognostic effects on medulloblastoma were found, and then these genes were subjected to multi-factor Cox regression analysis on each molecular subtype, and finally GNG3 was determined .The combination of CALCB and GCGR can predict the development of SHH well (p=0.0011, AUC=0.734), SOCS3 and HOXC10 can better predict the development of G4 (p=0.044, AUC=0.618), and the combination of ADCY8 and LHX3 can predict G3 Development (p=0.034, AUC=0.675).ConclusionThis report showed a possible evidence that OS-related features of various molecular subtypes of medulloblastoma can independently predict the prognosis of patients with each subtype of medulloblastoma, and provide new therapeutic targets for them.

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PPARG, GNG12, and CD19 are Potential Independent Predictors of Central Nerve Recurrence in Childhood Acute Lymphoblastic Leukemia

10.21203/rs.3.rs-963371/v1 ◽

2021 ◽

Author(s):

Shan Zhang ◽

Yansong Tu ◽

Qianmiao Wu ◽

Huijun Chen ◽

Huaijun Tu ◽

...

Keyword(s):

Regression Analysis ◽

Acute Lymphoblastic Leukemia ◽

Phase I ◽

Cox Regression ◽

Lymphoblastic Leukemia ◽

Validation Group ◽

Hub Genes ◽

Cox Regression Analysis ◽

Cox Analysis ◽

Target Database

Abstract Objective: To identify biomarkers that can predict the recurrence of the central nervous system (CNS) in children with acute lymphoblastic leukemia (ALL), and establish a prediction model. Materials and Methods: The transcriptome and clinical data collected by the Children's Oncology Group (COG) collaboration group in the Phase II study (use for test group) and Phase I study (use for validation group) of ALL in children were downloaded from the TARGET database. Transcriptome data were analyzed by bioinformatics method to identify core (hub) genes and establish a risk assessment model. Univariate Cox analysis was performed on each clinical data, and multivariate Cox regression analysis was performed on the obtained results and risk score. The children ALL phase I samples collected by the COG collaboration group in the TARGET database were used for verification. Results: A total of 1230 differentially expressed genes were screened out between the CNS relapsed and non-relapsed groups. Univariate multivariate Cox analysis of 10 hub genes identified showed that PPARG (HR=0.78, 95%CI=0.67-0.91, p=0.007), CD19 (HR=1.15, 95%CI=1.05-1.26, p=0.003) and GNG12 (HR=1.25, 95%CI=1.04-1.51, p=0.017) had statistical differences. The risk score was statistically significant in univariate (HR=3.06, 95%CI=1.30-7.19, p=0.011) and multivariate (HR=1.81, 95%CI=1.16-2.32, p=0.046) Cox regression analysis. The survival analysis results of the high and low-risk groups were different when the validation group was substituted into the model (p=0.018). In addition, the CNS involvement grading status at first diagnosis CNS3 vs. CNS1 (HR=5.74, 95%CI=2.01-16.4, p=0.001), T cell vs B cell (HR=1.63, 95% CI=1.06-2.49, p=0.026) were also statistically significant. Conclusions: PPARG, GNG12, and CD19 may be predictors of CNS relapse in childhood ALL.

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