Identification of Biomarkers Related to Prognosis of Glioblastoma and Their Correlation with Immune Infiltration Cells

Abstract Glioblastoma (GBM) is the most malignant of all known intracranial tumors, meanwhile most patients have a poor prognosis. In order to improve the poor prognosis of GBM patients as much as possible, it is specifically significant to identify biomarkers related to the gene diagnosis and gene therapy. Herein, a total of 343 GBM specimens and 259 non-tumor specimens were collected from four Gene Expression Omnibus (GEO) datasets and TCGA database, and then analyzed the differentially expressed genes (DEGs) from the above data. Through Venn diagram analysis, 54 common upregulated DEGs and 22 common downregulated DEGs were triumphantly recognized. On account of the degree of formation communication in Protein-protein interaction network (PPIN), the 10 upregulated central genes had been ranked, incorporating LOX, IGFBP3, CD44, TIMP1, FN1, VEGFA, POSTN, COL1A1, COL1A2 and COL3A1. By combining the expression levels and the clinical features of GBM, four hub genes (TIMP1, FN1, POSTN and LOX) were significantly up-regulated and related to poor prognosis. Meanwhile, univariate and multivariate Cox regression analysis results suggested that TIMP1 could be one of the independent prognostic factors for GBM patients. Subsequently, TIMP1 was particularly correlated with the immune marker of macrophage M1, macrophage M2, Neutrophils, tumor associated macrophage and Tregs. In conclusion, these findings investigated that TIMP1 might be a new biomarker to determine prognosis and immune infiltration of GBM patients.

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Identification and validation of a hub gene prognostic index for hepatocellular carcinoma

Future Oncology ◽

10.2217/fon-2020-1112 ◽

2021 ◽

Author(s):

Q Shi ◽

Z Meng ◽

XX Tian ◽

YF Wang ◽

WH Wang

Keyword(s):

Hepatocellular Carcinoma ◽

Cox Regression ◽

Prognostic Index ◽

Interaction Network ◽

Gene Signature ◽

Gene Expression Omnibus ◽

Cox Regression Analysis ◽

Protein Protein Interaction ◽

Normal Tissues ◽

Significant Independent Prognostic Factor

Aims: We aim to provide new insights into the mechanisms of hepatocellular carcinoma (HCC) and identify key genes as biomarkers for the prognosis of HCC. Materials & methods: Differentially expressed genes between HCC tissues and normal tissues were identified via the Gene Expression Omnibus tool. The top ten hub genes screened by the degree of the protein nodes in the protein–protein interaction network also showed significant associations with overall survival in HCC patients. Results: A prognostic model containing a five-gene signature was constructed to predict the prognosis of HCC via multivariate Cox regression analysis. Conclusion: This study identified a novel five-gene signature ( CDK1, CCNB1, CCNB2, BUB1 and KIF11) as a significant independent prognostic factor.

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CXCL10 is a Tumor Microenvironment and Immune Infiltration Related Prognostic Biomarker in Pancreatic Adenocarcinoma

Frontiers in Molecular Biosciences ◽

10.3389/fmolb.2021.611508 ◽

2021 ◽

Vol 8 ◽

Author(s):

Huimin Huang ◽

Wangxiao Zhou ◽

Renpin Chen ◽

Bingfeng Xiang ◽

Shipeng Zhou ◽

...

Keyword(s):

Tumor Microenvironment ◽

Pancreatic Adenocarcinoma ◽

Cox Regression ◽

Predictive Biomarker ◽

Prognostic Indicator ◽

The Cancer Genome Atlas ◽

Cox Regression Analysis ◽

Immune Infiltration ◽

Protein Protein Interaction ◽

Novel Biomarkers

Pancreatic adenocarcinoma (PAAD) is the 10th most common cancer worldwide and the outcomes for patients with the disease remain extremely poor. Precision biomarkers are urgently needed to increase the efficiency of early diagnosis and to improve the prognosis of patients. The tumor microenvironment (TME) and tumor immune infiltration are thought to impact the occurrence, progression, and prognosis of PAAD. Novel biomarkers excavated originating from the TME and immune infiltration may be effective in predicting the prognosis of PAAD patients. In the current study, the ESTIMATE and CIBERSORT algorithms were applied to estimate the division of immune and stromal components and the proportion of tumor-infiltrating immune cells in 182 PAAD cases downloaded from The Cancer Genome Atlas database. Intersection analyses of the Protein-Protein Interaction networks and Cox regression analysis identified the chemokine (CXC-motif) ligand 10 (CXCL10) as a predictive biomarker. We verified that CXCL10 in the TME negatively correlates with prognosis in PAAD and positively correlates with tumor cell differentiation. GSE62452 from the GEO database and cumulative survival analysis were performed to validate CXCL10 expression as an independent prognostic indicator. We also found that memory B cells, regulatory T cells, and macrophages M0 and M1 were correlated with the expression of CXCL10 indicating that expression of CXCL10 influenced the immune activity of the TME. Our data suggest that CXCL10 is beneficial as a prognostic indicator in PAAD patients and highlights the potential for immune targeted therapy in the treatment of PAAD.

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Identification of genes that influence poor prognosis in osteosarcoma

10.21203/rs.3.rs-506161/v1 ◽

2021 ◽

Author(s):

Yao Lu ◽

Yanli Li ◽

Xueliang Zeng ◽

Bei Li ◽

Qiongjun Xie ◽

...

Keyword(s):

Gene Expression ◽

Poor Prognosis ◽

Bone Cancer ◽

Interaction Network ◽

Venn Diagram ◽

Future Research ◽

Protein Protein Interaction ◽

Normal Tissues ◽

Kaplan Meier ◽

Long Term Prognosis

Abstract Objectives Osteosarcoma (OS) is the most common primary bone cancer in children and adolescents. At present, the 5-year overall survival rate of OS patients is about 65%, and the long-term prognosis is still not ideal. The study was designed to screen genes that could contribute to the poor prognosis of OS and explore their potential pathogenic mechanisms. Methods The gene expression profile of the GSE94805 dataset from the GEO database, containing data from 12 U2OS cell samples, including four control, four quiescent, and four senescent samples was obtained. Co-expressed differentially expressed genes (DEGs) in OS U2OS cells were selected using the GEO2R tool and Venn diagram analysis. Next, using the STRING, Cytoscap, and Molecular Complex Detection (MCODE) plug-in, the related protein-protein interaction network among upregulated genes was analyzed. Moreover, Kaplan-Meier plots were used to analyze the relationship between the identified genes and OS prognosis. Genes significantly associated with worse prognosis were evaluated using the Gene Expression Profiling Interactive Analysis. Results Thirteen genes were confirmed to be significantly more expressed in OS than in normal tissues. Five genes (AURKB, EXO1, KIF4A, KIF15, and MCM4) were found to influence OS prognosis. Conclusion We identified five core genes related to the prognosis of OS and constructed a clinical prediction model for OS. Our data may provide a reference for future research on mechanisms, clinical diagnosis, and treatment of OS.

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Construction and Comprehensive Analyses of a METTL5-Associated Prognostic Signature With Immune Implication in Lung Adenocarcinomas

Frontiers in Genetics ◽

10.3389/fgene.2020.617174 ◽

2021 ◽

Vol 11 ◽

Author(s):

Sijin Sun ◽

Kailun Fei ◽

Guochao Zhang ◽

Juhong Wang ◽

Yannan Yang ◽

...

Keyword(s):

Poor Prognosis ◽

Cox Regression ◽

Prognostic Score ◽

Enrichment Analysis ◽

Gene Expression Omnibus ◽

Gene Set Enrichment Analysis ◽

Functional Enrichment ◽

Normal Lung ◽

Cox Regression Analysis ◽

Tcga Dataset

For lung adenocarcinoma (LUAD), patients of different stages have strong heterogeneity, and their overall prognosis varies greatly. Thus, exploration of novel biomarkers to better clarify the characteristics of LUAD is urgent. Multi-omics information of LUAD patients were collected form TCGA. Three independent LUAD cohorts were obtained from gene expression omnibus (GEO). A multi-omics correlation analysis of METTL5 was performed in TCGA dataset. To build a METTL5-associated prognostic score (MAPS). Spathial and random forest methods were first applied for feature selection. Then, LASSO was implemented to develop the model in TCGA cohort. The prognostic value of MAPS was validated in three independent GEO datasets. Finally, functional annotation was conducted using gene set enrichment analysis (GSEA) and the abundances of infiltrated immune cells were estimated by ImmuCellAI algorithm. A total of 901 LUAD patients were included. The expression of METTL5 in LUAD was significantly higher than that in normal lung tissue. And high expression of METTL5 indicated poor prognosis in all different stages (P < 0.001, HR = 1.81). Five genes (RAC1, C11of24, METTL5, RCCD1, and SLC7A5) were used to construct MAPS and MAPS was significantly correlated with poor prognosis (P < 0.001, HR = 2.15). Furthermore, multivariate Cox regression analysis suggested MAPS as an independent prognostic factor. Functional enrichment revealed significant association between MAPS and several immune components and pathways. This study provides insights into the potential significance of METTL5 in LUAD and MAPS can serve as a promising biomarker for LUAD.

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Breakthrough Mucormycosis Developing on Mucorales-Active Antifungals Portrays a Poor Prognosis in Patients with Hematologic Cancer

Journal of Fungi ◽

10.3390/jof7030217 ◽

2021 ◽

Vol 7 (3) ◽

pp. 217

Author(s):

Dierdre B. Axell-House ◽

Sebastian Wurster ◽

Ying Jiang ◽

Andreas Kyvernitakis ◽

Russell E. Lewis ◽

...

Keyword(s):

Amphotericin B ◽

Symptom Onset ◽

Poor Prognosis ◽

Treatment Initiation ◽

Cox Regression ◽

Severe Neutropenia ◽

Apache Ii Score ◽

Cell Transplant ◽

Hematopoietic Stem ◽

Cox Regression Analysis

Although breakthrough mucormycosis (BT-MCR) is known to develop on mold-active antifungals without Mucorales activity, it can also occur while on Mucorales-active antifungals. Herein, we retrospectively compared the characteristics and outcomes of patients with hematologic malignancies (HMs) or hematopoietic stem cell transplant (HSCT) who developed BT-MCR on mold-active antifungals with or without Mucorales activity. Of the patients developing BT-MCR, 16 were on Mucorales-active antifungals (9 isavuconazole, 6 posaconazole, 1 amphotericin B), and 87 were on other mold-active agents (52 voriconazole, 22 echinocandins, 8 itraconazole, 5 echinocandin + voriconazole). Both groups were largely comparable in clinical characteristics. Patients developing BT-MCR while on Mucorales-active antifungals had higher 42-day mortality, from either symptom onset (63% versus 25%, p = 0.006) or treatment initiation (69% versus 39%, p = 0.028). In multivariate Cox regression analysis, exposure to Mucorales-active antifungals prior to BT-MCR had a hazard ratio of 2.40 (p = 0.015) for 42-day mortality from treatment initiation and 4.63 (p < 0.001) for 42-day mortality from symptom onset. Intensive care unit (ICU) admission and APACHE II score at diagnosis, non-recovered severe neutropenia, active HM, and amphotericin B/caspofungin combination treatment were additional independent predictors of 42-day mortality. In summary, BT-MCR on Mucorales-active antifungals portrays poor prognosis in HM/HSCT patients. Moreover, improvements in early diagnosis and treatment are urgently needed in these patients.

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Differential Expression of mRNAs in Peripheral Blood Related to Prodrome and Progression of Alzheimer’s Disease

BioMed Research International ◽

10.1155/2020/4505720 ◽

2020 ◽

Vol 2020 ◽

pp. 1-10

Author(s):

Weishuang Xue ◽

Jinwei Li ◽

Kailei Fu ◽

Weiyu Teng

Keyword(s):

Alzheimer’S Disease ◽

Alzheimer's Disease ◽

Peripheral Blood ◽

Bioinformatics Analysis ◽

Gene Expression Omnibus ◽

Venn Diagram ◽

Ppi Network ◽

Hub Genes ◽

Protein Protein Interaction ◽

Ppi Networks

Alzheimer’s disease (AD) is a chronic progressive neurodegenerative disease that affects the quality of life of elderly individuals, while the pathogenesis of AD is still unclear. Based on the bioinformatics analysis of differentially expressed genes (DEGs) in peripheral blood samples, we investigated genes related to mild cognitive impairment (MCI), AD, and late-stage AD that might be used for predicting the conversions. Methods. We obtained the DEGs in MCI, AD, and advanced AD patients from the Gene Expression Omnibus (GEO) database. A Venn diagram was used to identify the intersecting genes. Gene Ontology (GO) and Kyoto Gene and Genomic Encyclopedia (KEGG) were used to analyze the functions and pathways of the intersecting genes. Protein-protein interaction (PPI) networks were constructed to visualize the network of the proteins coded by the related genes. Hub genes were selected based on the PPI network. Results. Bioinformatics analysis indicated that there were 61 DEGs in both the MCI and AD groups and 27 the same DEGs among the three groups. Using GO and KEGG analyses, we found that these genes were related to the function of mitochondria and ribosome. Hub genes were determined by bioinformatics software based on the PPI network. Conclusions. Mitochondrial and ribosomal dysfunction in peripheral blood may be early signs in AD patients and related to the disease progression. The identified hub genes may provide the possibility for predicting AD progression or be the possible targets for treatments.

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Expression of LOX Suggests Poor Prognosis in Gastric Cancer

Frontiers in Medicine ◽

10.3389/fmed.2021.718986 ◽

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.

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Identification and validation of immunotypes for clear cell RCC prognosis

10.21203/rs.3.rs-847352/v1 ◽

2021 ◽

Author(s):

Zhaolin Yang ◽

Jiale Zhou ◽

Yizheng Xue ◽

Yu Zhang ◽

Kaijun Zhou ◽

...

Keyword(s):

Overall Survival ◽

Cox Regression ◽

Clear Cell ◽

Survival Rates ◽

Prognostic Index ◽

Gene Expression Omnibus ◽

The Cancer Genome Atlas ◽

Cox Regression Analysis ◽

Immune Genes ◽

Metastasis Model

Abstract Purpose To develop an immunotype-based prognostic model for predicting the overall survival (OS) of patients with clear cell renal carcinoma (ccRCC). We explored novel immunotypes of patients with ccRCC, particularly those associated with overall survival. A risk-metastasis model was constructed by integrating the immunotypes with immune genes and used to test the accuracy of the immunotype model. Patients and Methods Patient cohort data were obtained from The Cancer Genome Atlas (TCGA) database, Gene Expression Omnibus (GEO) database, Renji database, and Surveillance, Epidemiology, and End Results (SEER) database. We employed the R software to select 3 immune cells and construct an immunotype-based prediction model. Immune genes selected using random Forest Algorithm were validated by immunohistochemistry (IHC). The H&L risk-metastasis model was constructed to assess the accuracy of the immunotype model through Multivariate COX regression analysis. Result Patients with ccRCC were categorized into immunotype H subgroup and immunotype L subgroup based on the overall survival rates. The immunotypes were found to be the independent prognostic index for ccRCC prognosis. As such, we constructed a new immunotypes-based SSIGN model. Three immune genes associated with difference between immunotype H and L were identified. An H&L risk-metastasis model was constructed to evaluate the accuracy of the immunotype model. Compared to the W-Risk-metastasis model which did not incorporate immunotypes, the H&L risk-metastasis model was more precise in predicting the survival of ccRCC patients. Conclusion The established immunotype model can effectively predict the survival of ccRCC patients. Except for mast cells, T cells and macrophages are positively associated with the overall survival of patients. The three immune genes identified, herein, can predict the survival rate of ccRCC patients, and expression of these immune genes is strongly linked to poor survival. The new SSIGN model provides an accurate tool for predicting the survival of ccRCC patients. H&L risk-metastasis model can effectively predict the risk of tumor metastasis.

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A Novel Six-mRNA Signature Predicts Survival of Patients With Glioblastoma Multiforme

Frontiers in Genetics ◽

10.3389/fgene.2021.634116 ◽

2021 ◽

Vol 12 ◽

Author(s):

Zhentao Liu ◽

Hao Zhang ◽

Hongkang Hu ◽

Zheng Cai ◽

Chengyin Lu ◽

...

Keyword(s):

Glioblastoma Multiforme ◽

Cox Regression ◽

Methylation Status ◽

Survival Rates ◽

Gene Expression Omnibus ◽

The Cancer Genome Atlas ◽

Survival Prediction ◽

Lasso Regression ◽

Cox Regression Analysis ◽

Score Model

Glioblastoma multiforme (GBM) is a devastating brain tumor and displays divergent clinical outcomes due to its high degree of heterogeneity. Reliable prognostic biomarkers are urgently needed for improving risk stratification and survival prediction. In this study, we analyzed genome-wide mRNA profiles in GBM patients derived from The Cancer Genome Atlas (TCGA) and Gene Expression Omnibus (GEO) databases to identify mRNA-based signatures for GBM prognosis with survival analysis. Univariate Cox regression model was used to evaluate the relationship between the expression of mRNA and the prognosis of patients with GBM. We established a risk score model that consisted of six mRNA (AACS, STEAP1, STEAP2, G6PC3, FKBP9, and LOXL1) by the LASSO regression method. The six-mRNA signature could divide patients into a high-risk and a low-risk group with significantly different survival rates in training and test sets. Multivariate Cox regression analysis confirmed that it was an independent prognostic factor in GBM patients, and it has a superior predictive power as compared with age, IDH mutation status, MGMT, and G-CIMP methylation status. By combining this signature and clinical risk factors, a nomogram can be established to predict 1-, 2-, and 3-year OS in GBM patients with relatively high accuracy.

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Identification and Validation of Immune-Related LncRNA Prognostic Signature for Lung Adenocarcinoma

Frontiers in Genetics ◽

10.3389/fgene.2021.681277 ◽

2021 ◽

Vol 12 ◽

Author(s):

Guomin Wu ◽

Qihao Wang ◽

Ting Zhu ◽

Linhai Fu ◽

Zhupeng Li ◽

...

Keyword(s):

Regression Analysis ◽

Lung Adenocarcinoma ◽

Clinical Features ◽

Cox Regression ◽

Differential Expression Analysis ◽

Enrichment Analysis ◽

Gene Set Enrichment Analysis ◽

Prognostic Signature ◽

Cox Regression Analysis ◽

Immune Infiltration

This study aimed to establish a prognostic risk model for lung adenocarcinoma (LUAD). We firstly divided 535 LUAD samples in TCGA-LUAD into high-, medium-, and low-immune infiltration groups by consensus clustering analysis according to immunological competence assessment by single-sample gene set enrichment analysis (ssGSEA). Profile of long non-coding RNAs (lncRNAs) in normal samples and LUAD samples in TCGA was used for a differential expression analysis in the high- and low-immune infiltration groups. A total of 1,570 immune-related differential lncRNAs in LUAD were obtained by intersecting the above results. Afterward, univariate COX regression analysis and multivariate stepwise COX regression analysis were conducted to screen prognosis-related lncRNAs, and an eight-immune-related-lncRNA prognostic signature was finally acquired (AL365181.2, AC012213.4, DRAIC, MRGPRG-AS1, AP002478.1, AC092168.2, FAM30A, and LINC02412). Kaplan–Meier analysis and ROC analysis indicated that the eight-lncRNA-based model was accurate to predict the prognosis of LUAD patients. Simultaneously, univariate COX regression analysis and multivariate COX regression analysis were undertaken on clinical features and risk scores. It was illustrated that the risk score was a prognostic factor independent from clinical features. Moreover, immune data of LUAD in the TIMER database were analyzed. The eight-immune-related-lncRNA prognostic signature was related to the infiltration of B cells, CD4+ T cells, and dendritic cells. GSEA enrichment analysis revealed significant differences in high- and low-risk groups in pathways like pentose phosphate pathway, ubiquitin mediated proteolysis, and P53 signaling pathway. This study helps to treat LUAD patients and explore molecules related to LUAD immune infiltration to deeply understand the specific mechanism.

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