Exploration of Various Roles of Hypoxia Genes in Osteosarcoma

Abstract Background: Osteosarcoma is a primary malignant tumor that often metastasizes in orthopedic diseases. Although multi-drug chemotherapy and surgical treatment have significantly improved the survival and prognosis of patients with osteosarcoma, the survival rate is still very low due to frequent metastases in patients with osteosarcoma. In-depth exploration of the relationship between various influencing factors of osteosarcoma is very important for screening promising therapeutic targets. Methods: This study used multivariate COX regression analysis to select the hypoxia genes SLC2A1 and FBP1 in patients with osteosarcoma, and used the expression of these two genes to divide the patients with osteosarcoma into high-risk and low-risk groups. Then, we first constructed a prognostic model based on the patient's risk value, and compared the survival difference between the high expression group and the low expression group. Second, in the high expression group and the low expression group, compare the differences in tumor invasion and inflammatory gene expression between the two groups of immune cells. Finally, the ferroptosis-related genes with differences between the high expression group and the low expression group were screened, and the correlation between these genes was analyzed. Results: In the high-risk group, immune cells with higher tumor invasiveness, macrophages M0 and immune cells with lower invasiveness included: mast cell resting, regulatory T cells (Tregs) and monocytes. Finally, among genes related to ferroptosis, we found AKR1C2, AKR1C1 and ALOX15 that may be related to hypoxia. These ferroptosis-related genes were discovered for the first time in osteosarcoma. Among them, the hypoxia gene FBP1 is positively correlated with the ferroptosis genes AKR1C1 and ALOX15, and the hypoxia gene SLC2A1 is negatively correlated with the ferroptosis genes AKR1C2, AKR1C1 and ALOX15. Conclusion: This study constructed a prognostic model based on hypoxia-related genes SLC2A1 and FBP1 in patients with osteosarcoma, and explored their correlation with immune cells, inflammatory markers and ferroptosis-related genes. This indicates that SLC2A1 and FBP1 are promising targets for osteosarcoma research.

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Association between Renal Podocalyxin Expression and Renal Dysfunction in Patients with Diabetic Nephropathy: A Single-Center, Retrospective Case-Control Study

BioMed Research International ◽

10.1155/2020/7350781 ◽

2020 ◽

Vol 2020 ◽

pp. 1-8

Author(s):

Rongzhen Wang ◽

Can Yao ◽

Feng Liu

Keyword(s):

Diabetic Nephropathy ◽

Retrospective Study ◽

Cox Regression ◽

High Expression ◽

Renal Outcome ◽

Control Group ◽

Creatinine Ratio ◽

Cox Regression Analysis ◽

High Expression Group ◽

Low Expression

This retrospective study investigated whether podocalyxin expression in renal biopsies and urine of patients with diabetic nephropathy (DN) is associated with renal function. This retrospective study included 32 patients with nephropathy, secondary to type 2 diabetes treated at the First Hospital of Lanzhou University (January 2010 to January 2015). Compared with the control group, the DN group had a significantly lower renal expression of podocalyxin and higher urinary podocalyxin/creatinine ratio. Patients with DN were divided into the high and low expression groups according to podocalyxin expression in renal tissues. Patients in the low expression group had longer diabetes duration, lower plasma albumin and eGFR, higher glycated hemoglobin (HbA1c), 24 h urinary protein, serum creatinine, and urinary podocalyxin/creatinine ratio, and more severe glomerular, tubulointerstitial, and renal interstitial inflammation than patients in the high expression group (all P<0.01). The renal survival rate was significantly lower in the low expression group than in the high expression group (P<0.01). Single-factor Cox regression analysis showed that reduced podocalyxin expression and increased urinary podocalyxin excretion were associated with poor renal outcome. Measuring podocalyxin levels in renal tissues and urine could help evaluate the progression of DN.

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A risk stratification model based on four novel biomarkers predicts prognosis for patients with renal cell carcinoma

World Journal of Surgical Oncology ◽

10.1186/s12957-020-02046-9 ◽

2020 ◽

Vol 18 (1) ◽

Author(s):

Shigehisa Kubota ◽

Tetsuya Yoshida ◽

Susumu Kageyama ◽

Takahiro Isono ◽

Takeshi Yuasa ◽

...

Keyword(s):

Prognostic Model ◽

Cox Regression ◽

Treatment Strategies ◽

Immunohistochemical Analysis ◽

Cancer Specific Survival ◽

Clinical Prognosis ◽

Cox Regression Analysis ◽

Model Based ◽

Novel Biomarkers ◽

Metastatic Rcc

Abstract Background Accurate prediction of the prognosis of RCC using a single biomarker is challenging due to the genetic heterogeneity of the disease. However, it is essential to develop an accurate system to allow better patient selection for optimal treatment strategies. ARL4C, ECT2, SOD2, and STEAP3 are novel molecular biomarkers identified in earlier studies as survival-related genes by comprehensive analyses of 43 primary RCC tissues and RCC cell lines. Methods To develop a prognostic model based on these multiple biomarkers, the expression of four biomarkers ARL4C, ECT2, SOD2, and STEAP3 in primary RCC tissue were semi-quantitatively investigated by immunohistochemical analysis in an independent cohort of 97 patients who underwent nephrectomy, and the clinical significance of these biomarkers were analyzed by survival analysis using Kaplan-Meier curves. The prognostic model was constructed by calculation of the contribution score to prognosis of each biomarker on Cox regression analysis, and its prognostic performance was validated. Results Patients whose tumors had high expression of the individual biomarkers had shorter cancer-specific survival (CSS) from the time of primary nephrectomy. The prognostic model based on four biomarkers segregated the patients into a high- and low-risk scored group according to defined cut-off value. This approach was more robust in predicting CSS compared to each single biomarker alone in the total of 97 patients with RCC. Especially in the 36 metastatic RCC patients, our prognostic model could more accurately predict early events within 2 years of diagnosis of metastasis. In addition, high risk-scored patients with particular strong SOD2 expression had a much worse prognosis in 25 patients with metastatic RCC who were treated with molecular targeting agents. Conclusions Our findings indicate that a prognostic model based on four novel biomarkers provides valuable data for prediction of clinical prognosis and useful information for considering the follow-up conditions and therapeutic strategies for patients with primary and metastatic RCC.

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Identification of Prognostic Gene Signature Associated with Tumor Microenvironment of Cholangiocarcinoma

10.21203/rs.3.rs-524410/v1 ◽

2021 ◽

Author(s):

Jixiang Cao ◽

Xi Chen ◽

Guang Lu ◽

Haowei Wang ◽

Xinyu Zhang ◽

...

Keyword(s):

Regression Analysis ◽

Tumor Microenvironment ◽

Immune Cells ◽

Prognostic Model ◽

Cox Regression ◽

Gene Signature ◽

Risk Scores ◽

Tumor Infiltration ◽

Prognostic Signature ◽

Cox Regression Analysis

Abstract Background: Cholangiocarcinoma (CCA) is the most common malignancy of the biliary tract with a dismal prognosis. Increasing evidence suggests that tumor microenvironment (TME) is closely associated with cancer prognosis. However, the prognostic signature for CCA based on TME has not yet been reported. This study aimed to develop a TME-related prognostic signature for accurately predicting the prognosis of patients with CCA. Methods: Based on the TCGA database, we calculated the stromal and immune scores using the ESTIMATE algorithm to assess TME in stromal and immune cells derived from CCA. TME-related differentially expressed genes were identified, followed by functional enrichment analysis and PPI network analysis. Univariate Cox regression analysis, Lasso Cox regression model and multivariable Cox regression analysis were performed to identify and construct the TME-related prognostic gene signature. Gene Set Enrichment Analyses (GSEA) was performed to further investigate the potential molecular mechanisms. The correlations between the risk scores and tumor infiltration immune cells were analyzed using Tumor Immune Estimation Resource (TIMER) database. Results: A total of 784 TME-related differentially expressed genes (DEGs) were identified, which were mainly enriched in immune-related processes and pathways. Among these TME-related DEGs, A novel two‑gene signature (including GAD1 and KLRB1) was constructed for CCA prognosis prediction. The AUC of the prognostic model for predicting the survival of patients at 1-, 2-, and 3- years was 0.811, 0.772, and 0.844, respectively. Cox regression analysis showed that the two‑gene signature was an independent prognostic factor. Based on the risk scores of the prognostic model, CCA patients were divided into high- and low-risk groups, and patients with high-risk score had shorter survival time than those with low-risk score. Furthermore, we found that the risk scores were negatively correlated with TME-scores and the number of several tumor infiltration immune cells, including B cells and CD4+ T cells. Conclusion: Our study established a novel TME-related gene signature to predict the prognosis of patients with CCA. This might provide a new understanding of the potential relationship between TME and CCA prognosis, and serve as a prognosis stratification tool for guiding personalized treatment of CCA patients.

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The Correlation Between SPP1 and Immune Escape of EGFR Mutant Lung Adenocarcinoma Was Explored by Bioinformatics Analysis

Frontiers in Oncology ◽

10.3389/fonc.2021.592854 ◽

2021 ◽

Vol 11 ◽

Author(s):

Yi Zheng ◽

Shiying Hao ◽

Cheng Xiang ◽

Yaguang Han ◽

Yanhong Shang ◽

...

Keyword(s):

Lung Adenocarcinoma ◽

Poor Prognosis ◽

Egfr Mutation ◽

Cox Regression ◽

Checkpoint Inhibitors ◽

High Expression ◽

Wild Type ◽

Cox Regression Analysis ◽

High Expression Group ◽

Mutant Egfr

BackgroundImmune checkpoint inhibitors have achieved breakthrough efficacy in treating lung adenocarcinoma (LUAD) with wild-type epidermal growth factor receptor (EGFR), leading to the revision of the treatment guidelines. However, most patients with EGFR mutation are resistant to immunotherapy. It is particularly important to study the differences in tumor microenvironment (TME) between patients with and without EGFR mutation. However, relevant research has not been reported. Our previous study showed that secreted phosphoprotein 1 (SPP1) promotes macrophage M2 polarization and PD-L1 expression in LUAD, which may influence response to immunotherapy. Here, we assessed the role of SPP1 in different populations and its effects on the TME.MethodsWe compared the expression of SPP1 in LUAD tumor and normal tissues, and in samples with wild-type and mutant EGFR. We also evaluated the influence of SPP1 on survival. The LUAD data sets were downloaded from TCGA and CPTAC databases. Clinicopathologic characteristics associated with overall survival in TCGA were assessed using Cox regression analysis. GSEA revealed that several fundamental signaling pathways were enriched in the high SPP1 expression group. We applied CIBERSORT and xCell to calculate the proportion and abundance of tumor-infiltrating immune cells (TICs) in LUAD, and compared the differences in patients with high or low SPP1 expression and wild-type or mutant EGFR. In addition, we explored the correlation between SPP1 and CD276 for different groups.ResultsSPP1 expression was higher in LUAD tumor tissues and in people with EGFR mutation. High SPP1 expression was associated with poor prognosis. Univariate and multivariate cox analysis revealed that up-regulated SPP1 expression was independent indicator of poor prognosis. GSEA showed that the SPP1 high expression group was mainly enriched in immunosuppressed pathways. In the SPP1 high expression group, the infiltration of CD8+ T cells was lower and M2-type macrophages was higher. These results were also observed in patients with EGFR mutation. Furthermore, we found that the SPP1 expression was positively correlated with CD276, especially in patients with EGFR mutation.ConclusionSPP1 levels might be a useful marker of immunosuppression in patients with EGFR mutation, and could offer insight for therapeutics.

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The Family Members of Mrps Expression and significance in Childhood Acute Lymphoblastic Leukemia

Blood ◽

10.1182/blood.v124.21.5191.5191 ◽

2014 ◽

Vol 124 (21) ◽

pp. 5191-5191

Author(s):

Baoling Qiu ◽

Dong WU ◽

Qi Zhou ◽

Dan Hong ◽

Jian Pan ◽

...

Keyword(s):

High Risk ◽

Complete Remission ◽

Risk Group ◽

High Risk Group ◽

High Expression ◽

Initial Stage ◽

High Expression Group ◽

Standard Risk ◽

Median Expression ◽

Low Expression

Abstract Objective Multidrug resistance-associated proteins 1 to 6 have been reported involved in a large number of tumors and have a close correlation with tumor multi-drug resistance. In this work, we detect the MRP2-6 genes expression in childhood acute lymphoblastic leukemia (ALL) by Q-RT-PCR and explore their clinical significance. Methods 156 patients at different stages of ALL were enrolled in this study and treated by the protocol (CCLG-2008) during 2012 to 2013, including 67 cases at initial stage, 70 cases at complete remission, and 9 cases at relapse, 10 patients diagnosed as idiopathic thrombocytopenic purpura (ITP) as control. MRP1-6 genes’ expressions were detected using real-time quantitative PCR (QRT-PCR)and their clinical significance was analyzed by the SPSS software 16.0. P value below 0.05 was regarded as statistic significance. Results The median expression of MRP1 was 5.82 and 8.49 for initial and relapse group,respectively, which was statistic higher than that at complete remission, the latter was 1.99. MRP1 expression level had close correlation with ALL risk, the median of MRP1 expression was 4.28, 5.62 and 7.56 for standard-risk group (SR), intermediate-risk group (IR) and high-risk group (HR), respectively. Initial ALL children were divided into two groups including high expression group and low expression group by the median expression, the rate of sensitivity of blast cells to prednisone on 7th day was 70.6% in high expression group (n=34), which was statistic lower than that in low expression group which was 90.9% (n=33, P=0.035). The rate of complete remission on 33th day in high expression group was 64.7%, while 87.9% in low expression group, which showed a significant difference between them (P=0.026). The rate of complete remission on 15th day in high expression group was 68.8%, and 69.7% in low expression group, which showed no significance between them (P=0.664). The transcription level of MRP1 in initial group of T-ALL (median=7.71) was statistic higher than that in B-ALL (median=5.18) (P=0.007). Correlation analysis indicated that mRNA expression level of MRP1 didn’t show any relationship with gender, age, WBC count, hemoglobin, platelet and blast percentage in bone narrow and peripheral blood at diagnosis. The median expression of MRP2 at initial stage was highest, higher than that at relapse and complete remission, but did not reach statistic significance. However, the median expression of MRP3 at initial stage was highest and statistic higher than complete remission. MRP4 and MRP5 showed a similar pattern in their expression, namely, high expression for relapse, intermediate expression for initial stage and low expression at complete remission which reached statistic significance. The median expression of MRP6 for relapse was 2.003, which was higher than initial and complete remission group, but the differences among them were not significant. The median of MRP2 and MRP5 expression for intermediate-risk group (IR) (Median MRP2=4.622, Median MRP5=1.712) was higher than standard- risk group (SR) (Median MRP2=3.279, Median MRP5=1.277) and high risk group (HR) (Median MRP2=2.145, Median MRP5=1.673). However, there was no statistical significance among them. The median of MRP4 expression was increase continually with the risk of ALL, but did not reach statistical significance among them; the median of MRP6 expression for high-risk group (HR) was higher than standard-risk group (0.8812 vs 0.6205) and intermediate-risk group (0.8812 vs 0.4053), but the differences among them were not significant either. Initial ALL children were divided into two group including high expression group and low expression group by median expression, and evaluated their prediction on the treatment response on 7th, 15th and 33th day. The results revealed no significant difference between them, neither between B-ALL and T-ALL. Single factor analysis showed that MRP2 has relationship with platelet count at diagnosis and MRP4 has relationship with gender. Conclusions In children ALL, the expression of MRP1 is closely related with immunophenotyping, treatment response, hazard level and disease relapse which was a poor biomarker for ALL prognosis. MRP2-6 has a different expression pattern. MRP4 and MRP6 mRNA expression showed a close relation with relapse. Disclosures No relevant conflicts of interest to declare.

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

Frontiers in Cell and Developmental Biology ◽

10.3389/fcell.2021.796729 ◽

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.

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Development and Validation of an Mesenchymal-Related Long Non-Coding RNA Prognostic Model in Glioma

Frontiers in Oncology ◽

10.3389/fonc.2021.726745 ◽

2021 ◽

Vol 11 ◽

Author(s):

Kebing Huang ◽

Xiaoyu Yue ◽

Yinfei Zheng ◽

Zhengwei Zhang ◽

Meng Cheng ◽

...

Keyword(s):

High Risk ◽

Prognostic Model ◽

Cox Regression ◽

Risk Groups ◽

Low Risk ◽

Gene Set Enrichment Analysis ◽

Migration And Invasion ◽

Aberrant Expression ◽

Cox Regression Analysis ◽

Genome Atlas

Glioma is well known as the most aggressive and prevalent primary malignant tumor in the central nervous system. Molecular subtypes and prognosis biomarkers remain a promising research area of gliomas. Notably, the aberrant expression of mesenchymal (MES) subtype related long non-coding RNAs (lncRNAs) is significantly associated with the prognosis of glioma patients. In this study, MES-related genes were obtained from The Cancer Genome Atlas (TCGA) and the Ivy Glioblastoma Atlas Project (Ivy GAP) data sets of glioma, and MES-related lncRNAs were acquired by performing co-expression analysis of these genes. Next, Cox regression analysis was used to establish a prognostic model, that integrated ten MES-related lncRNAs. Glioma patients in TCGA were divided into high-risk and low-risk groups based on the median risk score; compared with the low-risk groups, patients in the high-risk group had shorter survival times. Additionally, we measured the specificity and sensitivity of our model with the ROC curve. Univariate and multivariate Cox analyses showed that the prognostic model was an independent prognostic factor for glioma. To verify the predictive power of these candidate lncRNAs, the corresponding RNA-seq data were downloaded from the Chinese Glioma Genome Atlas (CGGA), and similar results were obtained. Next, we performed the immune cell infiltration profile of patients between two risk groups, and gene set enrichment analysis (GSEA) was performed to detect functional annotation. Finally, the protective factors DGCR10 and HAR1B, and risk factor SNHG18 were selected for functional verification. Knockdown of DGCR10 and HAR1B promoted, whereas knockdown of SNHG18 inhibited the migration and invasion of gliomas. Collectively, we successfully constructed a prognostic model based on a ten MES-related lncRNAs signature, which provides a novel target for predicting the prognosis for glioma patients.

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The Construction and Exploration of the CeRNA Network and Patterns of Tumor-infiltrating Immune Cells in Kidney Renal Clear Cell Carcinoma

10.21203/rs.3.rs-113464/v2 ◽

2021 ◽

Author(s):

Weiwei Jia ◽

Pengjia Li ◽

Mingxia Ma ◽

Xiaochen Niu ◽

Lina Bai ◽

...

Keyword(s):

T Cells ◽

Regression Analysis ◽

Mast Cells ◽

Immune Cells ◽

Prognostic Model ◽

Cox Regression ◽

Cox Regression Analysis ◽

Cerna Network ◽

Follicular Helper ◽

Lncrna Malat1

Abstract KIRC is the malignant tumor with the highest incidence and poor prognosis in renal cell carcinoma. We want to explore the possible mechanisms of KIRC and effective prognostic-related biomarkers. The sequencing information of 3 types of RNA (mRNA, lncRNA and miRNA) in 539 cases of KIRC tissues and 72 cases of normal tissues is obtained from the TCGA database. Methods such as univariate Cox regression analysis, lasso regression screening, and multivariate Cox regression analysis were used to construct a prognostic model based on the CeRNA network. There are 3074 mRNAs, 359 lncRNAs and 132 miRNAs differentially expressed that have been identified through differential analysis. A complete mRNA-miRNA-lncRNA (SIX1-hsa-miR-200b-3p-MALAT1) network was obtained based on the CeRNA network. The CIBERSORT algorithm was used to analyze the degree of infiltration of 22 kinds of immune cells from each sample of KIRC. Construction of a prognostic model based on tumor-infiltrating immune cells, 2 immune cells (Mast cells resting, T cells follicular helper) were identified by constructing a prognostic model. There was a negative correlation between lncRNA MALAT1 and Mast cells resting (R= -0.27, P < 0.001); while there was a positive correlation between lncRNA MALAT1 and T cells follicular helper (R = 0.23, P < 0.001).

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Construction and Validation of Prognostic Markers of Liver Cancer Based on Autophagy Genes

Anti-Cancer Agents in Medicinal Chemistry ◽

10.2174/1871520621666210329100052 ◽

2021 ◽

Vol 21 ◽

Author(s):

Dawei Zhou ◽

Junchen Wan ◽

Jiang Luo ◽

Yuhao Tao

Keyword(s):

Regression Analysis ◽

High Risk ◽

Liver Cancer ◽

Cancer Patients ◽

Prognostic Model ◽

Cox Regression ◽

Risk Group ◽

Primary Liver Cancer ◽

Risk Groups ◽

Cox Regression Analysis

Background: Liver cancer is one of the most common diseases in the world. At present, the mechanism of autophagy genes in liver cancer is not very clear. Therefore, it is meaningful to study the role and prognostic value of autophagy genes in liver cancer. Objective: The purpose of this study is to conduct a bioinformatics analysis of autophagy genes related to primary liver cancer to establish a prognostic model of primary liver cancer based on autophagy genes. Results: Through difference analysis, 31 differential autophagy genes were screened out and then analyzed by GO and KEGG analysis. At the same time, we built a PPI network. To optimize the evaluation of the prognosis of liver cancer patients, we integrated multiple autophagy genes to establish a prognostic model. By using univariate cox regression analysis, 15 autophagy genes related to prognosis were screened out. Then we included these 15 genes into the Least Absolute Shrinkage and Selection Operator (LASSO), and performed multi-factor cox regression analysis on the 9 selected genes to construct a prognostic model. The risk score of each patient was calculated based on 4 genes(BIRC5, HSP8, SQSTM1, and TMEM74) which participated in the establishing of the model, then the patients were divided into high-risk groups and low-risk groups. In the multivariate cox regression analysis, the risk score was the independent prognostic factors (HR=1.872, 95%CI=1.544-2.196, P<0.001). Survival analysis showed that the survival time of the low-risk group was significantly longer than that of the high-risk group. Combining clinical characteristics and autophagy genes, we constructed a nomogram for predicting prognosis. The external dataset GSE14520 proved that the nomogram has a good prediction for individual patients with primary liver cancer. Conclusion: This study provided potential autophagy-related markers for liver cancer patients to predict their prognosis and revealed part of the molecular mechanism of liver cancer autophagy. At the same time, the certain gene pathways and protein pathways related to autophagy may provide some inspiration for the development of anticancer drugs.

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Development and validation of an immune-related gene prognostic model for stomach adenocarcinoma

Bioscience Reports ◽

10.1042/bsr20201012 ◽

2020 ◽

Vol 40 (10) ◽

Author(s):

Ming Wu ◽

Yu Xia ◽

Yadong Wang ◽

Fei Fan ◽

Xian Li ◽

...

Keyword(s):

Prognostic Model ◽

Clinical Decision Making ◽

Cox Regression ◽

Risk Group ◽

Prognostic Models ◽

Related Gene ◽

Cox Regression Analysis ◽

Model Based ◽

Immune Related Gene ◽

Stomach Adenocarcinoma

Abstract Purpose: Stomach adenocarcinoma (STAD) is one of the most common malignant tumors, and its occurrence and prognosis are closely related to inflammation. The aim of the present study was to identify gene signatures and construct an immune-related gene (IRG) prognostic model in STAD using bioinformatics analysis. Methods: RNA sequencing data from healthy samples and samples with STAD, IRGs, and transcription factors were analyzed. The hub IRGs were identified using univariate and multivariate Cox regression analyses. Using the hub IRGs, we constructed an IRG prognostic model. The relationships between IRG prognostic models and clinical data were tested. Results: A total of 289 differentially expressed IRGs and 20 prognostic IRGs were screened with a threshold of P<0.05. Through multivariate stepwise Cox regression analysis, we developed a prognostic model based on seven IRGs. The prognostic model was validated using a GEO dataset (GSE 84437). The IRGs were significantly correlated with the clinical outcomes (age, histological grade, N, and M stage) of STAD patients. The infiltration abundances of dendritic cells and macrophages were higher in the high-risk group than in the low-risk group. Conclusions: Our results provide novel insights into the pathogenesis of STAD. An IRG prognostic model based on seven IRGs exhibited the predictive value, and have potential application value in clinical decision-making and individualized treatment.

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