scholarly journals An Autophagy-Related Long Noncoding RNA Signature Contributes to Poor Prognosis in Colorectal Cancer

2020 ◽  
Vol 2020 ◽  
pp. 1-13
Author(s):  
Jingsun Wei ◽  
Xiaoxu Ge ◽  
Yang Tang ◽  
Yucheng Qian ◽  
Wei Lu ◽  
...  
Keyword(s):  
Colorectal Cancer ◽  
Regression Analysis ◽  
Long Noncoding Rna ◽  
Noncoding Rna ◽  
Poor Prognosis ◽  
Cox Regression ◽  
Low Risk ◽  
Gene Set Enrichment Analysis ◽  
Primary Tumors ◽  
Cox Regression Analysis

Purpose. Colorectal cancer is one of the most common malignant primary tumors, prone to metastasis, and associated with a poor prognosis. As autophagy is closely related to the development and treatment of colorectal cancer, we investigated the potential prognostic value of long noncoding RNA (lncRNA) associated with autophagy in colorectal cancer. Methods. In this study, we acquired information on the expression of lncRNAs in colorectal cancer from the Cancer Genome Atlas (TCGA) database and found that 860 lncRNAs were associated with autophagy-related genes. Subsequently, univariate Cox regression analysis was used to investigate 32 autophagy-related lncRNAs linked to colon cancer prognosis. Subsequently, eight of the 32 autophagy-related lncRNAs (i.e., long intergenic nonprotein coding RNA 1503 [LINC01503], ZEB1 antisense RNA 1 [ZEB1-AS1], AC087481.3, AC008760.1, AC073896.3, AL138756.1, AL022323.1, and TNFRSF10A-AS1) were selected through multivariate Cox regression analysis. Based on these autophagy-related lncRNAs, a risk signature was constructed, and the patients were divided into high- and low-risk groups. Results. The high-risk group’s overall survival time was significantly shorter than that of the low-risk group p < 0.0001 . Receiver operating characteristic curve analysis was performed to further confirm the validity of the model (area under the curve: 0.689). Moreover, multivariate regression suggested that the risk score was a significant prognostic risk factor in colorectal cancer. Gene set enrichment analysis showed that these gene sets are significantly enriched in cancer-related pathways, such as Kirsten rat sarcoma viral oncogene homolog (KRAS) signaling. Conclusion. The risk signature of eight autophagy-related lncRNAs has prognostic potential for colorectal cancer. These autophagy-related lncRNAs may play a vital role in the biology of colorectal cancer.

scholarly journals Expression of LOX Suggests Poor Prognosis in Gastric Cancer

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

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

scholarly journals A Signature of Autophagy-Related Long Non-coding RNA to Predict the Prognosis of Breast Cancer

2021 ◽  
Vol 12 ◽  
Author(s):  
Xiaoping Li ◽  
Jishang Chen ◽  
Qihe Yu ◽  
Hui Huang ◽  
Zhuangsheng Liu ◽  
...  
Keyword(s):  
Breast Cancer ◽  
Regression Analysis ◽  
High Risk ◽  
Cox Regression ◽  
Enrichment Analysis ◽  
Low Risk ◽  
Gene Set Enrichment Analysis ◽  
Lasso Regression ◽  
Cox Regression Analysis ◽  
Risk Scoring

Background: A surge in newly diagnosed breast cancer has overwhelmed the public health system worldwide. Joint effort had beed made to discover the genetic mechanism of these disease globally. Accumulated research has revealed autophagy may act as a vital part in the pathogenesis of breast cancer.Objective: Aim to construct a prognostic model based on autophagy-related lncRNAs and investigate their potential mechanisms in breast cancer.Methods: The transcriptome data and clinical information of patients with breast cancer were obtained from The Cancer Genome Atlas (TCGA) database. Autophagy-related genes were obtained from the Human Autophagy Database (HADb). Long non-coding RNAs (lncRNAs) related to autophagy were acquired through the Pearson correlation analysis. Univariate Cox regression analysis as well as the least absolute shrinkage and selection operator (LASSO) regression analysis were used to identify autophagy-related lncRNAs with prognostic value. We constructed a risk scoring model to assess the prognostic significance of the autophagy-related lncRNAs signatures. The nomogram was then established based on the risk score and clinical indicators. Through the calibration curve, the concordance index (C-index) and receiver operating characteristic (ROC) curve analysis were evaluated to obtain the model's predictive performance. Subgroup analysis was performed to evaluate the differential ability of the model. Subsequently, gene set enrichment analysis was conducted to investigate the potential functions of these lncRNAs.Results: We attained 1,164 breast cancer samples from the TCGA database and 231 autophagy-related genes from the HAD database. Through correlation analysis, 179 autophagy-related lncRNAs were finally identified. Univariate Cox regression analysis and LASSO regression analysis further screened 18 prognosis-associated lncRNAs. The risk scoring model was constructed to divide patients into high-risk and low-risk groups. It was found that the low-risk group had better overall survival (OS) than those of the high-risk group. Then, the nomogram model including age, tumor stage, TNM stage and risk score was established. The evaluation index (C-index: 0.78, 3-year OS AUC: 0.813 and 5-year OS AUC: 0.785) showed that the nomogram had excellent predictive power. Subgroup analysis showed there were difference in OS between high-risk and low-risk patients in different subgroups (stage I-II, ER positive, Her-2 negative and non-TNBC subgroups; all P &lt; 0.05). According to the results of gene set enrichment analysis, these lncRNAs were involved in the regulation of multicellular organismal macromolecule metabolic process in multicellular organisms, nucleotide excision repair, oxidative phosphorylation, and TGF-β signaling pathway.Conclusions: We identified 18 autophagy-related lncRNAs with prognostic value in breast cancer, which may regulate tumor growth and progression in multiple ways.

scholarly journals Development and Validation of a Novel Hypoxia-Related Long Noncoding RNA Model With Regard to Prognosis and Immune Features in Breast Cancer

2021 ◽  
Vol 9 ◽  
Author(s):  
Peng Gu ◽  
Lei Zhang ◽  
Ruitao Wang ◽  
Wentao Ding ◽  
Wei Wang ◽  
...  
Keyword(s):  
Breast Cancer ◽  
High Risk ◽  
Long Noncoding Rna ◽  
Noncoding Rna ◽  
Prognostic Model ◽  
Cox Regression ◽  
Risk Group ◽  
High Risk Group ◽  
Low Risk ◽  
Cox Regression Analysis

Background: Female breast cancer is currently the most frequently diagnosed cancer in the world. This study aimed to develop and validate a novel hypoxia-related long noncoding RNA (HRL) prognostic model for predicting the overall survival (OS) of patients with breast cancer.Methods: The gene expression profiles were downloaded from The Cancer Genome Atlas (TCGA) database. A total of 200 hypoxia-related mRNAs were obtained from the Molecular Signatures Database. The co-expression analysis between differentially expressed hypoxia-related mRNAs and lncRNAs based on Spearman’s rank correlation was performed to screen out 166 HRLs. Based on univariate Cox regression and least absolute shrinkage and selection operator Cox regression analysis in the training set, we filtered out 12 optimal prognostic hypoxia-related lncRNAs (PHRLs) to develop a prognostic model. Kaplan–Meier survival analysis, receiver operating characteristic curves, area under the curve, and univariate and multivariate Cox regression analyses were used to test the predictive ability of the risk model in the training, testing, and total sets.Results: A 12-HRL prognostic model was developed to predict the survival outcome of patients with breast cancer. Patients in the high-risk group had significantly shorter median OS, DFS (disease-free survival), and predicted lower chemosensitivity (paclitaxel, docetaxel) compared with those in the low-risk group. Also, the risk score based on the expression of the 12 HRLs acted as an independent prognostic factor. The immune cell infiltration analysis revealed that the immune scores of patients in the high-risk group were lower than those of the patients in the low-risk group. RT-qPCR assays were conducted to verify the expression of the 12 PHRLs in breast cancer tissues and cell lines.Conclusion: Our study uncovered dozens of potential prognostic biomarkers and therapeutic targets related to the hypoxia signaling pathway in breast cancer.

scholarly journals An autophagy-related long non-coding RNA signature for patients with colorectal cancer

2021 ◽  
Author(s):  
Dongyan Zhao ◽  
Xizhen Sun ◽  
Sidan Long ◽  
Shukun Yao
Keyword(s):  
Colorectal Cancer ◽  
Overall Survival ◽  
High Risk ◽  
Cox Regression ◽  
Risk Group ◽  
Expression Profiles ◽  
High Risk Group ◽  
Low Risk ◽  
Gene Set Enrichment Analysis ◽  
Cox Regression Analysis

AbstractAimLong non-coding RNAs (lncRNAs) have been identified to regulate cancers by controlling the process of autophagy and by mediating the post-transcriptional and transcriptional regulation of autophagy-related genes. This study aimed to investigate the potential prognostic role of autophagy-associated lncRNAs in colorectal cancer (CRC) patients.MethodsLncRNA expression profiles and the corresponding clinical information of CRC patients were collected from The Cancer Genome Atlas (TCGA) database. Based on the TCGA dataset, autophagy-related lncRNAs were identified by Pearson correlation test. Univariate Cox regression analysis and the least absolute shrinkage and selection operator analysis (LASSO) Cox regression model were performed to construct the prognostic gene signature. Gene set enrichment analysis (GSEA) was used to further clarify the underlying molecular mechanisms.ResultsWe obtained 210 autophagy-related genes from the whole dataset and found 1187 lncRNAs that were correlated with the autophagy-related genes. Using Univariate and LASSO Cox regression analyses, eight lncRNAs were screened to establish an eight-lncRNA signature, based on which patients were divided into the low-risk and high-risk group. Patients’ overall survival was found to be significantly worse in the high-risk group compared to that in the low-risk group (log-rank p = 2.731E-06). ROC analysis showed that this signature had better prognostic accuracy than TNM stage, as indicated by the area under the curve. Furthermore, GSEA demonstrated that this signature was involved in many cancer-related pathways, including TGF-β, p53, mTOR and WNT signaling pathway.ConclusionsOur study constructed a novel signature from eight autophagy-related lncRNAs to predict the overall survival of CRC, which could assistant clinicians in making individualized treatment.

scholarly journals Comprehensive Analysis of Ferroptosis-Related LncRNAs in Breast Cancer Patients Reveals Prognostic Value and Relationship With Tumor Immune Microenvironment

2021 ◽  
Vol 8 ◽  
Author(s):  
Zhengjie Xu ◽  
Suxiao Jiang ◽  
Juan Ma ◽  
Desheng Tang ◽  
Changsheng Yan ◽  
...  
Keyword(s):  
Breast Cancer ◽  
T Cells ◽  
Regression Analysis ◽  
Cox Regression ◽  
Comprehensive Analysis ◽  
Low Risk ◽  
Gene Set Enrichment Analysis ◽  
Prognostic Signature ◽  
Immune Microenvironment ◽  
Cox Regression Analysis

Background: Breast cancer (BC) is a heterogeneous malignant tumor, leading to the second major cause of female mortality. This study aimed to establish an in-depth relationship between ferroptosis-related LncRNA (FRlncRNA) and the prognosis as well as immune microenvironment of the patients with BC.Methods: We downloaded and integrated the gene expression data and the clinical information of the patients with BC from The Cancer Genome Atlas (TCGA) database. The co-expression network analysis and univariate Cox regression analysis were performed to screen out the FRlncRNAs related to prognosis. A cluster analysis was adopted to explore the difference of immune microenvironment between the clusters. Furthermore, we determined the optimal survival-related FRLncRNAs for final signature by LASSO Cox regression analysis. Afterward, we constructed and validated the prediction models, which were further tested in different subgroups.Results: A total of 31 FRLncRNAs were filtrated as prognostic biomarkers. Two clusters were determined, and C1 showed better prognosis and higher infiltration level of immune cells, such as B cells naive, plasma cells, T cells CD8, and T cells CD4 memory activated. However, there were no significantly different clinical characters between the clusters. Gene Set Enrichment Analysis (GSEA) revealed that some metabolism-related pathways and immune-associated pathways were exposed. In addition, 12 FRLncRNAs were determined by LASSO analysis and used to construct a prognostic signature. In both the training and testing sets, patients in the high-risk group had a worse survival than the low-risk patients. The area under the curves (AUCs) of receiver operator characteristic (ROC) curves were about 0.700, showing positive prognostic capacity. More notably, through the comprehensive analysis of heatmap, we regarded LINC01871, LINC02384, LIPE-AS1, and HSD11B1-AS1 as protective LncRNAs, while LINC00393, AC121247.2, AC010655.2, LINC01419, PTPRD-AS1, AC099329.2, OTUD6B-AS1, and LINC02266 were classified as risk LncRNAs. At the same time, the patients in the low-risk groups were more likely to be assigned to C1 and had a higher immune score, which were consistent with a better prognosis.Conclusion: Our research indicated that the ferroptosis-related prognostic signature could be used as novel biomarkers for predicting the prognosis of BC. The differences in the immune microenvironment exhibited by BC patients with different risks and clusters suggested that there may be a complementary synergistic effect between ferroptosis and immunotherapy.

scholarly journals Identification and Validation of EMT-Related lncRNA Prognostic Signature for Colorectal Cancer

2021 ◽  
Vol 12 ◽  
Author(s):  
Danfeng Li ◽  
Xiaosheng Lin ◽  
Binlie Chen ◽  
Zhiyan Ma ◽  
Yongming Zeng ◽  
...  
Keyword(s):  
Colorectal Cancer ◽  
Overall Survival ◽  
Regression Analysis ◽  
Cox Regression ◽  
Epithelial Mesenchymal Transition ◽  
Clinical Information ◽  
Low Risk ◽  
Expression Data ◽  
Mesenchymal Transition ◽  
Cox Regression Analysis

Background: This study aimed to explore the biological functions and prognostic role of Epithelial-mesenchymal transition (Epithelial-mesenchymal transition)-related lncRNAs in colorectal cancer (CRC).Methods: The Cancer Genome Atlas database was applied to retrieve gene expression data and clinical information. An EMT-related lncRNA risk signature was constructed relying on univariate Cox regression, Least Absolute Shrinkage and Selector Operation (LASSO) and multivariate Cox regression analysis of the EMT-related lncRNA expression data and clinical information. Then, an individualized prognostic prediction model based on the nomogram was developed and the predictive accuracy and discriminative ability of the nomogram were determined by the receiver operating characteristic curve and calibration curve. Finally, a series of analyses, such as functional analysis and unsupervised cluster analysis, were conducted to explore the influence of independent lncRNAs on CRC.Results: A total of 581 patients were enrolled and an eleven-EMT-related lncRNA risk signature was identified relying on the comprehensive analysis of the EMT-related lncRNA expression data and clinical information in the training cohort. Then, risk scores were calculated to divide patients into high and low-risk groups, and the Kaplan-Meier curve analysis showed that low-risk patients tended to have better overall survival (OS). Multivariate Cox regression analysis indicated that the EMT-related lncRNA signature was significantly associated with prognosis. The results were subsequently confirmed in the validation dataset. Then, we constructed and validated a predictive nomogram for overall survival based on the clinical factors and risk signature. Functional characterization confirmed this signature could predict immune-related phenotype and was associated with immune cell infiltration (i.e., macrophages M0, M1, Tregs, CD4 memory resting cells, and neutrophils), tumor mutation burden (TMB).Conclusions: Our study highlighted the value of the 11-EMT-lncRNA signature as a predictor of prognosis and immunotherapeutic response in CRC.

scholarly journals Identification of Prognosis-Related RBPs to Reveal The Role of RNA Binding Proteins in the Progression and Prognosis of Wilms’ Tumor

2021 ◽  
Author(s):  
Xuejiao Qi ◽  
Shuyu Wang ◽  
Yihui Dong ◽  
Xiaojie Lin ◽  
Jingqiu Chen
Keyword(s):  
Regression Analysis ◽  
Cox Regression ◽  
Wilms Tumor ◽  
Prognostic Index ◽  
Low Risk ◽  
Gene Set Enrichment Analysis ◽  
Risk Scores ◽  
Clinical Parameters ◽  
Cox Regression Analysis ◽  
The Relationship

Abstract Background: Despite the various key functions of RBPs in posttranscriptional events, the mechanism of their influence on Wilms’ tumor has not been well elucidated. Therefore, we constructed the research to identify several RBPs related to Wilmes’ tumor progression and prognosis, for the better understanding of RBPs’ role in the occurrence and development of Wilmes’ tumor, and to provide effective reference targets for new drug development.Methods: A total of 127 samples of different clinical characteristics including gender, race and stage were selected from TARGET to carry out our study. After the gene functional enrichment pathways, univariate Cox regression analysis and lasso regression analysis were performed to test the prognostic effect of the differentially-expressed genes and establish the prognostic index . Further Cox regression analyses were utilized to identify the independence of our model and to analyze the relationship between our model and clinical parameters. What’s more, gene set enrichment analysis (GSEA) was also performed to elucidate the biological characteristics of genes involved in Wilms’ tumor. P< 0.05 was considered to be statistically significant.Results: 20 RBPs were statistically correlated with Wilms’ tumor. After the construction of a prognostic index , patients were divided into high-and low-risk scores group. Kaplan-Meier (K-M) analyses showed that patients with high risk scores possessed poorer survival probability than patients with low risk scores in both training group and test group. Furthermore, multivariate Cox regression analysis explored the relationship between our prognostic model and clinical parameters and confirmed that our model was an independent predicted factor for Wilms’ tumor. Conclusion: Our study clarifies the application of RBPs in the prognosis of Wilms’ tumor. We are confident that our risk scoring model can provide ideas for the development of new targets for broad-spectrum anticancer drugs and has great potential in clinical practice.

scholarly journals The Ferroptosis-Related Noncoding RNA Signature as a Novel Prognostic Biomarker in the Tumor Microenvironment, Immunotherapy, and Drug Screening of Gastric Adenocarcinoma

2021 ◽  
Vol 11 ◽  
Author(s):  
Xinming Chen ◽  
Zheng Zhu ◽  
Xiaoling Li ◽  
Xinyue Yao ◽  
Lianxiang Luo
Keyword(s):  
Tumor Microenvironment ◽  
Gastric Adenocarcinoma ◽  
Noncoding Rna ◽  
Cox Regression ◽  
Prognostic Biomarker ◽  
Risk Group ◽  
Expression Profiles ◽  
Low Risk ◽  
Gene Set Enrichment Analysis ◽  
Cox Regression Analysis

BackgroundFerroptosis is a new type of cell death different from apoptosis, necrosis, autophagy, and pyroptosis. This study aimed to explore the relationship between ferroptosis-related noncoding RNA (ncRNA) and gastric adenocarcinoma with regard to immunity and prognosis.MethodsFerroptosis-related ncRNA expression profiles and clinical pathology and overall survival information were collected from The Cancer Genome Atlas (TCGA) and the Gene Expression Omnibus database. The ferroptosis-related ncRNA signature was identified by Cox regression analysis and the least absolute shrinkage and selection operator analysis. The survival analysis, receiver operating characteristic (ROC) analysis, and decision curve analysis were adopted to evaluate the prognostic prediction performance of the signature. The correlation between risk and multiple clinical characteristics was analyzed using the chi-square test. The Gene Ontology, Kyoto Encyclopedia of Genes and Genomes, and gene set enrichment analysis were used for mining functions and pathways. The CIBERSORT, ssGSEA, and ESTIMATE algorithms were used to assess immune infiltration and the tumor microenvironment. The response of immunotherapy was predicted using the Submap algorithm, and the Connectivity Map and the ridge regression model were used to screen and evaluate drugs.ResultsA carcinogenic risk signature was constructed using five ferroptosis-related ncRNAs. It showed an extraordinary ability to predict the prognoses of patients with gastric adenocarcinoma [area under the ROC curve (AUC) after 6 years = 0.689; GSE84426, AUC after 6 years = 0.747]. The lower ferroptosis potential level and lower tumor mutation burden were related to the poor prognoses of patients. The high-risk group had more immune cell recruitment, and the overall effect of the anti-immune checkpoint immunotherapy was not as good as that of the low-risk group. The high- and low-risk groups were enriched in tumor- and immune-related pathways, respectively. The screened antitumor drugs, such as genistein, guanabenz, and betulinic acid, improved the survival of the patients.ConclusionsThe ferroptosis-related ncRNA signature is a potential carcinogenic prognostic biomarker of gastric adenocarcinoma.

scholarly journals Development And Validation of A Novel Hypoxia- And Immune-Related Prognostic Signature For Bone And Soft Tissue Sarcoma Patients

2021 ◽  
Author(s):  
Zhehong Li ◽  
Junqiang Wei ◽  
Honghong Zheng ◽  
Xintian Gan ◽  
Mingze Song ◽  
...  
Keyword(s):  
Regression Analysis ◽  
Immune Status ◽  
Cox Regression ◽  
Risk Groups ◽  
Clinicopathological Characteristics ◽  
Low Risk ◽  
Gene Set Enrichment Analysis ◽  
The Novel ◽  
Prognostic Signature ◽  
Cox Regression Analysis

Abstract Background: Hypoxia- and immune-status play an essential role in tumorigenesis and tumor development. This study sought to build a novel hypoxia- and immune-related signature to evaluate sarcoma patients' prognosis.Methods: Transcriptome data and clinicopathological characteristics of sarcoma patients were downloaded from the TARGET database. We grouped patients with three clusters by using t-SNE. We defined the three cluster as high-, medium-, and low-hypoxia clusters by K-M analysis and differential expression of target genes associated with the HIF-1 signaling pathway. Then we used the "limma" package to screen hypoxia-related differentially expressed genes (HRDEGs) in the high- and low-hypoxia clusters. We immediately assessed the immune status by using the single sample Gene Set Enrichment Analysis (ssGSEA) and divided the patients into high-, medium-, and low-immune clusters. Immune-related DEGs (IRDEGs) were filtered in the high- and low- immune groups. The intersection of HRDEGs and IRDEGs screened overlapping genes. We used a combination of Cox regression analysis and LASSO model to obtain prognosis-related genes and established a novel hypoxia- and immune-related prognostic signature for sarcoma patients. Combining clinicopathological characteristics of sarcoma patients, we evaluated the signature by univariate and multivariate Cox regression analysis. We further divided the patients into high- and low-risk groups based on the novel signature. Finally, we evaluated the differences in hypoxia status and the immune status in high- and low-risk groups.Results: We identified two genes associated with prognosis, CMA1 and IGDCC3. The novel Prognostic signature could be used as an independent prognostic factor for sarcoma patients. We distinguished patients more effectively by their different survival outcomes, immune cells' infiltration status, and immune-related markers.Conclusion: The hypoxia- and immune-related prognostic signature can be used to stratify the risk of sarcoma patients. Our study established a new prognostic signature and provides a potential prognostic markers for hypoxia- and immune-related therapy.

scholarly journals Upregulation of FGD6 Predicts Poor Prognosis in Gastric Cancer

2021 ◽  
Vol 8 ◽  
Author(s):  
Jianmin Zeng ◽  
Man Li ◽  
Huasheng Shi ◽  
Jianhui Guo
Keyword(s):  
Gastric Cancer ◽  
Regression Analysis ◽  
Prognostic Factor ◽  
Poor Prognosis ◽  
Cox Regression ◽  
Independent Prognostic Factor ◽  
Gene Set Enrichment Analysis ◽  
High Expression ◽  
Cox Regression Analysis ◽  
The Relationship

Background: The aim of this study was to investigate the prognostic significance of faciogenital dysplasia 6 (FGD6) in gastric cancer (GC).Methods: The data of GC patients from The Cancer Genome Atlas (TCGA) database were used for the primary study. Then, our data were validated by the GEO database and RuiJin cohort. The relationship between the FGD6 level and various clinicopathological features was analyzed by logistic regression and univariate Cox regression. Multivariate Cox regression analysis was used to evaluate whether FGD6 was an independent prognostic factor for survival of patients with GC. The relationship between FGD6 and overall survival time was explored by the Kaplan–Meier method. In addition, gene set enrichment analysis (GSEA) was performed to investigate the possible biological processes of FGD6.Results: The FGD6 level was significantly overexpressed in GC tissues, compared with adjacent normal tissues. The high expression of FGD6 was related to a high histological grade, stage, and T classification and poor prognosis of GC. Multivariate Cox regression analysis showed that FGD6 was an independent prognostic factor for survival of patients with GC. GSEA identified that the high expression of FGD6 was mainly enriched in regulation of actin cytoskeleton.Conclusion: FGD6 may be a prognostic biomarker for predicting the outcome of patients with GC.

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