Retinoic acid metabolism-related enzyme signature identified prognostic and immunotherapeutic efficiency in sarcoma

Abstract Background: Dynamic balance of retinoic acid metabolism plays a major role in a variety of biological functions including cell proliferation and differentiation, while its dysregulation often leads to cancer progression and disordered immunity. Targeting retinoic acid signaling has shown effectivity in re-educates tumor microenvironment so that they could enhance the efficacy of immunotherapies and received better outcome. However, a comprehensive analysis of retinoic acid metabolism abnormality in sarcoma is still lacking, which limits the development and application of related targeted drugs.Methods: The RA metabolism related enzymes data set was collected from several database. Then we systematically analyzed the molecular features of 19 retinoic acid metabolism-related enzymes based on TCGA/TARGET/GSE datasets and revealed two subtypes with distinct metabolic status and prognostic value. And we further generated a 7 genes signature to predict retinoic acid metabolism index based on LASSO-penalized Cox regression model.Results: Gene set enrichment analysis indicated a set of immune and oncogenic pathways were enriched in poor-prognosis group. Connectivity Map screened 56 potential small molecules specific to different sub-groups. Survival analysis demonstrated significant prognostic difference between high- and low-risk groups among all datasets. Several immune cells including CD8+ T cells, Treg cells, Monocytes, and Macrophages showed different abundance between these groups, and immune checkpoint blockade therapy response prediction indicated potential immunotherapeutic efficiency of poor-prognosis group.Conclusions: Taken together, our study elaborated two different retinoic acid metabolism status of sarcoma, which revealed the metabolic heterogeneity of sarcoma. Robust and powerful metabolic index risk model could provide insightful suggestions to explore the molecular functions and mechanisms of retinoic acid metabolism.

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Overexpressed XRCC2 as an independent risk factor for poor prognosis in glioma patients

Molecular Medicine ◽

10.1186/s10020-021-00316-0 ◽

2021 ◽

Vol 27 (1) ◽

Author(s):

Zhendong Liu ◽

Wang Zhang ◽

Xingbo Cheng ◽

Hongbo Wang ◽

Lu Bian ◽

...

Keyword(s):

Risk Factor ◽

Expression Pattern ◽

Poor Prognosis ◽

Independent Risk Factor ◽

Cox Regression ◽

Enrichment Analysis ◽

Gene Set Enrichment Analysis ◽

Cellular Mechanisms ◽

Molecular Features ◽

Meta Analyses

Abstract Background XRCC2, a homologous recombination-related gene, has been reported to be associated with a variety of cancers. However, its role in glioma has not been reported. This study aimed to find out the role of XRCC2 in glioma and reveal in which glioma-specific biological processes is XRCC2 involved based on thousands of glioma samples, thereby, providing a new perspective in the treatment and prognostic evaluation of glioma. Methods The expression characteristics of XRCC2 in thousands of glioma samples from CGGA and TCGA databases were comprehensively analyzed. Wilcox or Kruskal test was used to analyze the expression pattern of XRCC2 in gliomas with different clinical and molecular features. The effect of XRCC2 on the prognosis of glioma patients was explored by Kaplan–Meier and Cox regression. Gene set enrichment analysis (GSEA) revealed the possible cellular mechanisms involved in XRCC2 in glioma. Connectivity map (CMap) was used to screen small molecule drugs targeting XRCC2 and the expression levels of XRCC2 were verified in glioma cells and tissues by RT-qPCR and immunohistochemical staining. Results We found the overexpression of XRCC2 in glioma. Moreover, the overexpressed XRCC2 was associated with a variety of clinical features related to prognosis. Cox and meta-analyses showed that XRCC2 is an independent risk factor for the poor prognosis of glioma. Furthermore, the results of GSEA indicated that overexpressed XRCC2 could promote malignant progression through involved signaling pathways, such as in the cell cycle. Finally, doxazosin, quinostatin, canavanine, and chrysin were identified to exert anti-glioma effects by targeting XRCC2. Conclusions This study analyzed the expression pattern of XRCC2 in gliomas and its relationship with prognosis using multiple datasets. This is the first study to show that XRCC2, a novel oncogene, is significantly overexpressed in glioma and can lead to poor prognosis in glioma patients. XRCC2 could serve as a new biomarker for glioma diagnosis, treatment, and prognosis evaluation, thus bringing new insight into the management of glioma.

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Characterization of hypoxia signature to evaluate the tumor immune microenvironment and predict prognosis in glioma.

Journal of Clinical Oncology ◽

10.1200/jco.2020.38.15_suppl.e14534 ◽

2020 ◽

Vol 38 (15_suppl) ◽

pp. e14534-e14534

Author(s):

Shihong Wu ◽

Wanzun Lin ◽

Youliang Weng ◽

Yuhui Pan ◽

Zongwei Huang ◽

...

Keyword(s):

Survival Data ◽

Poor Prognosis ◽

Risk Model ◽

Malignant Gliomas ◽

High Risk Group ◽

Gene Set Enrichment Analysis ◽

Immune Microenvironment ◽

Molecular Features ◽

Dismal Prognosis ◽

Hypoxia Signature

e14534 Background: Glioma, the most common primary brain tumor, accounts for more than 50% of all primary brain tumors. Malignant gliomas, especially glioblastomas, are associated with a dismal prognosis. Hypoxia is a driver of the malignant phenotype in glioma; it triggers a cascade of immunosuppressive processes and malignant cellular responses (tumor progression, metastases, and resistance to chemoradiotherapy), which result in disease progression and poor prognosis. However, approaches to determine the extent of hypoxia in the tumor microenvironment are still unclear. Methods: Here, we enrolled 1626 glioma patients with RNA sequence and survival data in two independent cohorts, and developed a hypoxia risk model to reflect the immune microenvironment in glioma and predict prognosis. Results: High hypoxia risk score was associated with poor prognosis and indicated an immunosuppressive microenvironment. Hypoxia signature significantly correlated with clinical and molecular features and could serve as an independent prognostic factor for glioma patients. Moreover, Gene Set Enrichment Analysis showed that gene sets associated with the high-risk group were involved in carcinogenesis and immunosuppression signaling. Conclusions: In conclusion, we developed and validated a novel hypoxia risk model, which served as an independent prognostic indicator and reflected overall immune response intensity in the glioma microenvironment.

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Increased Expression Together with the Gene Regulation Network of NEIL3 Predicts Poor Prognosis and Correlates with Immune Infiltrates in Hepatocellular Carcinoma

10.21203/rs.2.23392/v1 ◽

2020 ◽

Author(s):

Bo Hu ◽

Xiao-Bo Yang ◽

Xinting Sang

Keyword(s):

Hepatocellular Carcinoma ◽

Gene Regulation ◽

Poor Prognosis ◽

Cox Regression ◽

Enrichment Analysis ◽

Gene Set Enrichment Analysis ◽

The Cancer Genome Atlas ◽

Immune Gene ◽

Data Set ◽

Gene Markers

Abstract Background: Abnormal Nei endonuclease VIII-like 3 (NEIL3)expression is associated with carcinogenesis. Methods: We used sequencing data from the Cancer Genome Atlas database, analyzed NEIL3 expression, gene regulation networks and the correlation with immune infiltrates in hepatocellular carcinoma (HCC). Clinicopathologic characteristics associated with overall survival in TCGA patients using Cox regression and the Kaplan-Meier method. Gene Set Enrichment Analysis was performed using TCGA data set. LinkedOmics was used to identify differential gene expression with NEIL3 and to analyze Gene Ontology and Kyoto Encyclopedia of Genes and Genomes pathways. Gene enrichment analysis examined target networks of kinases and transcription factors.Correlations between NEIL3 expression and cancer immune infiltrates and immune gene markers were analyzed by TIMER and GEPIA. Results: We found that overexpressed NEIL3 predicted poor prognosis. Functional network analysis suggested that NEIL3 regulates the DNA replication and cell cycle signaling via pathways involving several cancer-related kinases and E2F Transcription Factor 1.NEIL3 was also found to be associated with the infiltration of several immune cells. Conclusions: Our results demonstrate that data mining efficiently reveals information about NEIL3 expression, potential regulatory networks and the relationship with immune infiltration in HCC, laying a foundation for further study of the role of NEIL3 in carcinogenesis.

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The overexpression of FKBP4 in patients with lung adenocarcinoma predicts poor prognosis and tumor progression

10.21203/rs.3.rs-733351/v1 ◽

2021 ◽

Author(s):

Sha Tian ◽

Shang qing Wang ◽

Piao Zheng ◽

Xu Zhu ◽

Huan Han ◽

...

Keyword(s):

Lung Adenocarcinoma ◽

Signaling Pathways ◽

Cancer Progression ◽

Poor Prognosis ◽

Cox Regression ◽

Gene Set Enrichment Analysis ◽

The Cancer Genome Atlas ◽

Clinicopathological Parameters ◽

Poor Overall Survival ◽

Kaplan Meier

Abstract Background: The FK506-binding protein 4 ( FKBP4 ), a tumor-related gene, plays a vital role in tumorigenesis and cancer progression. The study is aimed to clarify the effect of FKBP4 in lung adenocarcinoma (LUAD). Methods: Relying on The Cancer Genome Atlas (TCGA) cohort, the FKBP4 expression difference between LUAD tissues and non-tumor tissues was first detected, and verified with public tissue microarrays (TMAs), clinical LUAD specimen cohort and Gene Expression Omnibus (GEO) cohort. Then, logistic regression analysis and chi-square test were applied to detect the correlation between FKBP4 expression and clinicopathological parameters. Kaplan-Meier survival analysis and Cox regression model were utilized to evaluate the effect of FKBP4 expression on survival. Signaling pathways related to LUAD were obtained via employing Gene Set Enrichment Analysis (GSEA). Results: The FKBP4 expression level in LUAD samples was dramatically higher than that in non-tumor samples. High FKBP4 expression in LUAD is associated with gender, pathological stage, T classification, lymph node metastasis and distant metastasis. The Kaplan-Meier curve indicated a poor prognosis for LUAD patients with high FKBP4 expression. Multivariate analysis suggested that the high FKBP4 expression was a vital independent predictor of poor overall survival (OS). GSEA showed that a total of 15 signaling pathways were enriched in samples with high FKBP4 expression phenotype. Conclusions: FKBP4 may be an oncogene in LUAD, and is promised to become a prognostic indicator and therapeutic target for LUAD.

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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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RNA N6-Methyladenosine Regulators Contribute to Tumor Immune Microenvironment and Have Clinical Prognostic Impact in Breast Cancer

Frontiers in Genetics ◽

10.3389/fgene.2021.650499 ◽

2022 ◽

Vol 12 ◽

Author(s):

Lan-Xin Mu ◽

You-Cheng Shao ◽

Lei Wei ◽

Fang-Fang Chen ◽

Jing-Wei Zhang

Keyword(s):

Breast Cancer ◽

Immune Cell ◽

Risk Model ◽

Cox Regression ◽

Gene Set Enrichment Analysis ◽

The Cancer Genome Atlas ◽

Prognostic Impact ◽

Immune Microenvironment ◽

Model Based ◽

Tumor Immune Microenvironment

Purpose: This study aims to reveal the relationship between RNA N6-methyladenosine (m6A) regulators and tumor immune microenvironment (TME) in breast cancer, and to establish a risk model for predicting the occurrence and development of tumors.Patients and methods: In the present study, we respectively downloaded the transcriptome dataset of breast cancer from Gene Expression Omnibus (GEO) database and The Cancer Genome Atlas (TCGA) database to analyze the mutation characteristics of m6A regulators and their expression profile in different clinicopathological groups. Then we used the weighted correlation network analysis (WGCNA), the least absolute shrinkage and selection operator (LASSO), and cox regression to construct a risk prediction model based on m6A-associated hub genes. In addition, Immune infiltration analysis and gene set enrichment analysis (GSEA) was used to evaluate the immune cell context and the enriched gene sets among the subgroups.Results: Compared with adjacent normal tissue, differentially expressed 24 m6A regulators were identified in breast cancer. According to the expression features of m6A regulators above, we established two subgroups of breast cancer, which were also surprisingly distinguished by the feature of the immune microenvironment. The Model based on modification patterns of m6A regulators could predict the patient’s T stage and evaluate their prognosis. Besides, the low m6aRiskscore group presents an immune-activated phenotype as well as a lower tumor mutation load, and its 5-years survival rate was 90.5%, while that of the high m6ariskscore group was only 74.1%. Finally, the cohort confirmed that age (p < 0.001) and m6aRiskscore (p < 0.001) are both risk factors for breast cancer in the multivariate regression.Conclusion: The m6A regulators play an important role in the regulation of breast tumor immune microenvironment and is helpful to provide guidance for clinical immunotherapy.

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The Prognostic Value of the m6A Score in Multiple Myeloma Based on Machine Learning

BioMedInformatics ◽

10.3390/biomedinformatics1030006 ◽

2021 ◽

Vol 1 (3) ◽

pp. 77-87

Author(s):

Gong Xiao ◽

Qiongjing Yuan ◽

Wei Wang

Keyword(s):

Multiple Myeloma ◽

Cancer Progression ◽

Risk Score ◽

Prognostic Value ◽

Risk Model ◽

Cox Regression ◽

Microarray Dataset ◽

Immune Checkpoints ◽

Bioinformatics Analyses ◽

Cox Regression Analysis

Background: Multiple myeloma (MM) is one of the most common cancers of the blood system. N6-methyladenosine (m6A) plays an important role in cancer progression. We aimed to investigate the prognostic relevance of the m6A score in multiple myeloma through a series of bioinformatics analyses. Methods: The microarray dataset GSE4581 and GSE57317 used in this study were downloaded from the Gene Expression Omnibus (GEO) database. The m6A score was calculated using the GSVA package. The Random forests, univariate Cox regression analysis and Lasso analyses were performed for the differentially expressed genes (DEGs). Kaplan–Meier analysis and an ROC curve were used to diagnose the effectiveness of the model. Results: The GSVA R software package was used to predict the function. A total of 21 m6A genes were obtained, and 286 DEGs were identified between high and low m6A score groups. The risk model was constructed and composed of PRX, LBR, RB1, FBXL19-AS1, ARSK, MFAP3L, SLC44A3, UNC119 and SHCBP1. Functional analysis of risk score showed that with the increase in the risk score, Activated CD4 T cells, Memory B cells and Type 2 T helper cells were highly infiltrated. Conclusions: Immune checkpoints such as HMGB1, TGFB1, CXCL9 and HAVCR2 were significantly positively correlated with the risk score. We believe that the m6A score has a certain prognostic value in multiple myeloma.

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A Tumor Progression Related 7-Gene Signature Indicates Prognosis and Tumor Immune Characteristics of Gastric Cancer

Frontiers in Oncology ◽

10.3389/fonc.2021.690129 ◽

2021 ◽

Vol 11 ◽

Author(s):

Fen Liu ◽

Zongcheng Yang ◽

Lixin Zheng ◽

Wei Shao ◽

Xiujie Cui ◽

...

Keyword(s):

Gastric Cancer ◽

High Risk ◽

Cancer Progression ◽

Risk Score ◽

Risk Model ◽

Cox Regression ◽

Hub Genes ◽

Cox Regression Analysis ◽

High Risk Patients ◽

Risk Patients

BackgroundGastric cancer is a common gastrointestinal malignancy. Since it is often diagnosed in the advanced stage, its mortality rate is high. Traditional therapies (such as continuous chemotherapy) are not satisfactory for advanced gastric cancer, but immunotherapy has shown great therapeutic potential. Gastric cancer has high molecular and phenotypic heterogeneity. New strategies for accurate prognostic evaluation and patient selection for immunotherapy are urgently needed.MethodsWeighted gene coexpression network analysis (WGCNA) was used to identify hub genes related to gastric cancer progression. Based on the hub genes, the samples were divided into two subtypes by consensus clustering analysis. After obtaining the differentially expressed genes between the subtypes, a gastric cancer risk model was constructed through univariate Cox regression, least absolute shrinkage and selection operator (LASSO) regression and multivariate Cox regression analysis. The differences in prognosis, clinical features, tumor microenvironment (TME) components and immune characteristics were compared between subtypes and risk groups, and the connectivity map (CMap) database was applied to identify potential treatments for high-risk patients.ResultsWGCNA and screening revealed nine hub genes closely related to gastric cancer progression. Unsupervised clustering according to hub gene expression grouped gastric cancer patients into two subtypes related to disease progression, and these patients showed significant differences in prognoses, TME immune and stromal scores, and suppressive immune checkpoint expression. Based on the different expression patterns between the subtypes, we constructed a gastric cancer risk model and divided patients into a high-risk group and a low-risk group based on the risk score. High-risk patients had a poorer prognosis, higher TME immune/stromal scores, higher inhibitory immune checkpoint expression, and more immune characteristics suitable for immunotherapy. Multivariate Cox regression analysis including the age, stage and risk score indicated that the risk score can be used as an independent prognostic factor for gastric cancer. On the basis of the risk score, we constructed a nomogram that relatively accurately predicts gastric cancer patient prognoses and screened potential drugs for high-risk patients.ConclusionsOur results suggest that the 7-gene signature related to tumor progression could predict the clinical prognosis and tumor immune characteristics of gastric cancer.

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Identification of Hub Prognosis-Associated Oxidative Stress Genes in Pancreatic Cancer Using Integrated Bioinformatics Analysis

Frontiers in Genetics ◽

10.3389/fgene.2020.595361 ◽

2020 ◽

Vol 11 ◽

Author(s):

Xin Qiu ◽

Qin-Han Hou ◽

Qiu-Yue Shi ◽

Hai-Xing Jiang ◽

Shan-Yu Qin

Keyword(s):

Oxidative Stress ◽

Gene Expression ◽

Pancreatic Cancer ◽

Cancer Progression ◽

Risk Model ◽

Cox Regression ◽

Expression Profiles ◽

The Cancer Genome Atlas ◽

Differentially Expressed ◽

Cox Regression Analysis

BackgroundIntratumoral oxidative stress (OS) has been associated with the progression of various tumors. However, OS has not been considered a candidate therapeutic target for pancreatic cancer (PC) owing to the lack of validated biomarkers.MethodsWe compared gene expression profiles of PC samples and the transcriptome data of normal pancreas tissues from The Cancer Genome Atlas (TCGA) and Genome Tissue Expression (GTEx) databases to identify differentially expressed OS genes in PC. PC patients’ gene profile from the Gene Expression Omnibus (GEO) database was used as a validation cohort.ResultsA total of 148 differentially expressed OS-related genes in PC were used to construct a protein-protein interaction network. Univariate Cox regression analysis, least absolute shrinkage, selection operator analysis revealed seven hub prognosis-associated OS genes that served to construct a prognostic risk model. Based on integrated bioinformatics analyses, our prognostic model, whose diagnostic accuracy was validated in both cohorts, reliably predicted the overall survival of patients with PC and cancer progression. Further analysis revealed significant associations between seven hub gene expression levels and patient outcomes, which were validated at the protein level using the Human Protein Atlas database. A nomogram based on the expression of these seven hub genes exhibited prognostic value in PC.ConclusionOur study provides novel insights into PC pathogenesis and provides new genetic markers for prognosis prediction and clinical treatment personalization for PC patients.

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Establishment of an Autophagy-Related Clinical Prognosis Model for Predicting the Overall Survival of Osteosarcoma

BioMed Research International ◽

10.1155/2021/5428425 ◽

2021 ◽

Vol 2021 ◽

pp. 1-17

Author(s):

Jianyi Li ◽

Xiaojie Tang ◽

Yukun Du ◽

Jun Dong ◽

Zheng Zhao ◽

...

Keyword(s):

Gene Expression ◽

Overall Survival ◽

Molecular Mechanisms ◽

Cox Regression ◽

Enrichment Analysis ◽

Risk Groups ◽

Gene Set Enrichment Analysis ◽

Data Set ◽

Primary Tumor Site ◽

Clinical Prognosis

Purpose. Osteosarcoma is the most common primary and highly invasive bone tumor in children and adolescents. The purpose of this study is to construct a multi-gene expression feature related to autophagy, which can be used to predict the prognosis of patients with osteosarcoma. Materials and methods. The clinical and gene expression data of patients with osteosarcoma were obtained from the target database. Enrichment analysis of autophagy-related genes related to overall survival (OS-related ARGs) screened by univariate Cox regression was used to determine OS-related ARGs function and signal pathway. In addition, the selected OS-related ARGs were incorporated into multivariate Cox regression to construct prognostic signature for the overall survival (OS) of osteosarcoma. Use the dataset obtained from the GEO database to verify the signature. Besides, gene set enrichment analysis (GSEA) were applied to further elucidate the molecular mechanisms. Finally, the nomogram is established by combining the risk signature with the clinical characteristics. Results. Our study eventually included 85 patients. Survival analysis showed that patients with low riskScore had better OS. In addition, 16 genes were included in OS-related ARGs. We also generate a prognosis signature based on two OS-related ARGs. The signature can significantly divide patients into low-risk groups and high-risk groups, and has been verified in the data set of GEO. Subsequently, the riskScore, primary tumor site and metastasis status were identified as independent prognostic factors for OS and a nomogram were generated. The C-index of nomogram is 0.789 (95% CI: 0.703~0.875), ROC curve and calibration chart shows that nomogram has a good consistency between prediction and observation of patients. Conclusions. ARGs was related to the prognosis of osteosarcoma and can be used as a biomarker of prognosis in patients with osteosarcoma. Nomogram can be used to predict OS of patients and improve treatment strategies.

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