scholarly journals PLXDC1 May Serve As A Target For Combining Antiangiogenic Therapy and Immunotherapy In Gastric Adenocarcinoma

2021 ◽  
Author(s):  
Xinwei Li ◽  
Yonfei Fan ◽  
Fang Su ◽  
Zishu Wang
Keyword(s):  
Risk Factor ◽  
Cox Regression ◽  
Risk Group ◽  
Antiangiogenic Therapy ◽  
High Risk Group ◽  
The Cancer Genome Atlas ◽  
Survival Prediction ◽  
Antiangiogenic Agents ◽  
Human Protein Atlas ◽  
Cox Analysis

Abstract Introduction Based on the immunosuppression of traditional antiangiogenic agents in the treatment of tumors and the newly proposed concept of antiangiogenic therapy combined with immunotherapy, this paper will mainly explore the prospects of PLXDC1 in stomach adenocarcinoma (STAD) regarding antiangiogenic therapy and immunotherapy.Methods First, the transcriptional and translational levels of PLXDC1 in STAD were analyzed using the Oncomine, The Cancer Genome Atlas (TCGA) and Human Protein Atlas databases and then univariate and multivariate Cox regression analyses were performed using TCGA data. Next, we explored the correlation between PLXDC1 and STAD immunity from multiple aspects. Finally, based on the acquisition of immunomodulators associated with PLXDC1 expression from TISIDB, we constructed PLXDC1-related immune prognostic signatures of four genes (NT5E, CTLA, TGFBR1, and CSF1R) and constructed a nomogram for predicting survival to analyze the clinical utility of PLXDC1 in immunotherapy.Results Our results demonstrated that PLXDC1 was highly expressed in STAD and that its high expression was associated with poor prognosis in STAD. Multivariate Cox analysis suggested that PLXDC1 could be used as an independent prognostic risk factor for STAD. The high-risk group for which we constructed PLXDC1-related immune prognostic signatures showed poorer prognosis compared to low-risk group, and the risk score of our model could be used as an independent risk factor for STAD prognosis. Moreover, the nomogram survival prediction system showed good accuracy of the constructed immune signatures.Conclusions In conclusion, PLXDC1 can serve as a biomarker for the diagnosis and treatment of STAD and it may also be a new target for STAD immunotherapy. Therefore, PLXDC1 can combine antiangiogenic therapy with immunotherapy.

scholarly journals Development of a prognostic signature of patients with esophagus adenocarcinoma by using immune-related genes

2021 ◽  
Vol 22 (1) ◽  
Author(s):  
Xiangxin Zhang ◽  
Liu Yang ◽  
Ming Kong ◽  
Jian Ma ◽  
Yutao Wei
Keyword(s):  
Risk Score ◽  
Cox Regression ◽  
Risk Group ◽  
Survival Rates ◽  
High Risk Group ◽  
Survival Prediction ◽  
Cox Regression Analysis ◽  
Related Data ◽  
Cox Analysis ◽  
Immune Related Genes

Abstract Background Esophageal adenocarcinoma (EAC) is an aggressive malignancy with a poor prognosis. The immune-related genes (IRGs) are crucial to immunocytes tumor infiltration. This study aimed to construct a IRG-related prediction signature in EAC. Methods The related data of EAC patients and IRGs were obtained from the TCGA and ImmPort database, respectively. The cox regression analysis constructed the prediction signature and explored the transcription factors regulatory network through the Cistrome database. TIMER database and CIBERSORT analytical tool were utilized to explore the immunocytes infiltration analysis. Results The prediction signature with 12 IRGs (ADRM1, CXCL1, SEMG1, CCL26, CCL24, AREG, IL23A, UCN2, FGFR4, IL17RB, TNFRSF11A, and TNFRSF21) was constructed. Overall survival (OS) curves indicate that the survival rate of the high-risk group is significantly shorter than the low-risk group (P = 7.26e−07), and the AUC of 1-, 3- and 5- year survival prediction rates is 0.871, 0.924, and 0.961, respectively. Compared with traditional features, the ROC curve of the risk score in the EAC patients (0.967) is significant than T (0.57), N (0.738), M (0.568), and Stage (0.768). Moreover, multivariate Cox analysis and Nomogram of risk score are indicated that the 1-year and 3-year survival rates of patients are accurate by the combined analysis of the risk score, Sex, M stage, and Stage (The AUC of 1- and 3-years are 0.911, and 0.853). Conclusion The 12 prognosis-related IRGs might be promising therapeutic targets for EAC.

scholarly journals Prognostic model of head and neck squamous cell carcinoma based on 12 ferroptosis-related genes

2021 ◽  
Author(s):  
Sijia Li ◽  
Hongyang Zhang ◽  
Wei Li
Keyword(s):  
Regression Analysis ◽  
Risk Score ◽  
Cox Regression ◽  
Risk Group ◽  
High Risk Group ◽  
Risk Scores ◽  
Cox Regression Analysis ◽  
Normal Tissues ◽  
Model Based ◽  
Cox Analysis

Abstract Background: The purpose of our study is establishing a model based on ferroptosis-related genes predicting the prognosis of patients with head and neck squamous cell carcinoma (HNSCC).Methods: In our study, transcriptome and clinical data of HNSCC patients were from The Cancer Genome Atlas, ferroptosis-related genes and pathways were from Ferroptosis Signatures Database. Differentially expressed genes (DEGs) were screened by comparing tumor and adjacent normal tissues. Functional enrichment analysis of DEGs, protein-protein interaction network and gene mutation examination were applied. Univariate Cox regression analysis and least absolute shrinkage and selection operator (LASSO) regression were used to identified DEGs. The model was constructed by multivariate Cox regression analysis and verified by Kaplan-Meier analysis. The relationship between risk scores and other clinical features was also analyzed. Univariate and multivariate Cox analysis was used to verified the independence of our model. The model was evaluated by receiver operating characteristic analysis and calculation of the area under the curve (AUC). A nomogram model based on risk score, age, gender and TNM stages was constructed.Results: We analyzed data including 500 tumor tissues and 44 adjacent normal tissues and 259 ferroptosis-related genes, then obtained 73 DEGs. Univariate Cox regression analysis screened out 16 genes related to overall survival, and LASSO analysis fingered out 12 of them with prognostic value. A risk score model based on these 12 genes was constructed by multivariate Cox regression analysis. According to the median risk score, patients were divided into high-risk group and low-risk group. The survival rate of high-risk group was significantly lower than that of low-risk group in Kaplan-Meier curve. Risk scores were related to T and grade. Univariate and multivariate Cox analysis showed our model was an independent prognostic factor. The AUC was 0.669. The nomogram showed high accuracy predicting the prognosis of HNSCC patients.Conclusion: Our model based on 12 ferroptosis-related genes performed excellently in predicting the prognosis of HNSCC patients. Ferroptosis-related genes may be promising biomarkers for HNSCC treatment and prognosis.

scholarly journals A novel signature of two long non-coding RNAs in BRCA mutant ovarian cancer to predict prognosis and efficiency of chemotherapy

2020 ◽  
Vol 13 (1) ◽  
Author(s):  
Yinglian Pan ◽  
Li Ping Jia ◽  
Yuzhu Liu ◽  
Yiyu Han ◽  
Qian Li ◽  
...  
Keyword(s):  
Ovarian Cancer ◽  
High Risk ◽  
Cox Regression ◽  
Risk Group ◽  
Gynecologic Cancer ◽  
High Risk Group ◽  
Low Risk ◽  
The Cancer Genome Atlas ◽  
Chemotherapy Treatment ◽  
Prognostic Signature

Abstract Background In this study we aimed to identify a prognostic signature in BRCA1/2 mutations to predict disease progression and the efficiency of chemotherapy ovarian cancer (OV), the second most common cause of death from gynecologic cancer in women worldwide. Methods Univariate Cox proportional-hazards and multivariate Cox regression analyses were used to identifying prognostic factors from data obtained from The Cancer Genome Atlas (TCGA) database. The area under the curve of the receiver operating characteristic curve was assessed, and the sensitivity and specificity of the prediction model were determined. Results A signature consisting of two long noncoding RNAs(lncRNAs), Z98885.2 and AC011601.1, was selected as the basis for classifying patients into high and low-risk groups (median survival: 7.2 years vs. 2.3 years). The three-year overall survival (OS) rates for the high- and low-risk group were approximately 38 and 100%, respectively. Chemotherapy treatment survival rates indicated that the high-risk group had significantly lower OS rates with adjuvant chemotherapy than the low-risk group. The one-, three-, and five-year OS were 100, 40, and 15% respectively in the high-risk group. The survival rate of the high-risk group declined rapidly after 2 years of OV chemotherapy treatment. Multivariate Cox regression associated with other traditional clinical factors showed that the 2-lncRNA model could be used as an independent OV prognostic factor. Analyses of data from the Kyoto Encyclopedia of Genes and Genomes (KEGG) and Gene Ontology (GO) indicated that these signatures are pivotal to cancer development. Conclusion In conclusion, Z98885.2 and AC011601.1 comprise a novel prognostic signature for OV patients with BRCA1/2 mutations, and can be used to predict prognosis and the efficiency of chemotherapy.

scholarly journals A Transcription Factor Signature Predicts Prognosis of Patients with Adrenocortical Carcinoma

2021 ◽  
Author(s):  
Jianyu Zhao ◽  
Bo Liu ◽  
Xiaoping Li
Keyword(s):  
Overall Survival ◽  
High Risk ◽  
Risk Score ◽  
Adrenocortical Carcinoma ◽  
Cox Regression ◽  
Risk Group ◽  
High Risk Group ◽  
Prognostic Signature ◽  
Abdominal Masses ◽  
Cox Analysis

Abstract Background: Adrenocortical carcinoma (ACC) is a rare endocrine cancer that manifests as abdominal masses and excessive steroid hormone levels. Transcription factors (TFs) deregulation is found to be involved in adrenocortical tumorigenesis and cancer progression. This study aimed to construct a TF-based prognostic signature for prediction of survival of ACC patients.Methods: The gene expression profile for ACC patients were downloaded from TCGA and GEO datasets. The univariate Cox analysis was applied to identify survival-related TFs and the LASSO Cox regression was conducted to construct the TF signature. The multivariate analysis was used to reveal the independent prognostic factors.Results: We identified a 13-TF prognostic signature comprised of CREB3L3, NR0B1, CENPA, FOXM1, E2F2, MYBL2, HOXC11, ZIC2, ZNF282, DNMT1, TCF3, ELK4, and KLF6 using the univariate Cox analysis and LASSO Cox regression. The risk score based on the TF-signature could classify patients into low- and high-risk group. Kaplan-Meier analyses showed that patients in the high-risk group had significantly shorter overall survival compared to the low-risk patients. ROC curves showed that the prognostic signature predicted the overall survival of ACC patients with good sensitivity and specificity. Furthermore, the TF-risk score was an independent prognostic factor.Conclusion: Taken together, we identified a 13-TF prognostic marker to predict overall survival in ACC patients.

scholarly journals Identification of a Five-Autophagy-Related-lncRNA Signature as a Novel Prognostic Biomarker for Hepatocellular Carcinoma

2021 ◽  
Vol 7 ◽  
Author(s):  
Xiaoyu Deng ◽  
Qinghua Bi ◽  
Shihan Chen ◽  
Xianhua Chen ◽  
Shuhui Li ◽  
...  
Keyword(s):  
Hepatocellular Carcinoma ◽  
High Risk ◽  
Cox Regression ◽  
Risk Group ◽  
High Risk Group ◽  
Gene Set Enrichment Analysis ◽  
The Cancer Genome Atlas ◽  
Prognostic Signature ◽  
Prediction Ability ◽  
Cox Regression Analysis

Although great progresses have been made in the diagnosis and treatment of hepatocellular carcinoma (HCC), its prognostic marker remains controversial. In this current study, weighted correlation network analysis and Cox regression analysis showed significant prognostic value of five autophagy-related long non-coding RNAs (AR-lncRNAs) (including TMCC1-AS1, PLBD1-AS1, MKLN1-AS, LINC01063, and CYTOR) for HCC patients from data in The Cancer Genome Atlas. By using them, we constructed a five-AR-lncRNA prognostic signature, which accurately distinguished the high- and low-risk groups of HCC patients. All of the five AR lncRNAs were highly expressed in the high-risk group of HCC patients. This five-AR-lncRNA prognostic signature showed good area under the curve (AUC) value (AUC = 0.751) for the overall survival (OS) prediction in either all HCC patients or HCC patients stratified according to several clinical traits. A prognostic nomogram with this five-AR-lncRNA signature predicted the 3- and 5-year OS outcomes of HCC patients intuitively and accurately (concordance index = 0.745). By parallel comparison, this five-AR-lncRNA signature has better prognosis accuracy than the other three recently published signatures. Furthermore, we discovered the prediction ability of the signature on therapeutic outcomes of HCC patients, including chemotherapy and immunotherapeutic responses. Gene set enrichment analysis and gene mutation analysis revealed that dysregulated cell cycle pathway, purine metabolism, and TP53 mutation may play an important role in determining the OS outcomes of HCC patients in the high-risk group. Collectively, our study suggests a new five-AR-lncRNA prognostic signature for HCC patients.

scholarly journals Identification and validation of a five-lncRNA prognostic signature related to Glioma using bioinformatics analysis

BMC Cancer ◽  
2021 ◽  
Vol 21 (1) ◽  
Author(s):  
Chunyu Zhang ◽  
Haitao Liu ◽  
Pengfei Xu ◽  
Yinqiu Tan ◽  
Yang Xu ◽  
...  
Keyword(s):  
Network Analysis ◽  
Cox Regression ◽  
Predictive Accuracy ◽  
Characteristic Curve ◽  
Prognostic Indicator ◽  
High Risk Group ◽  
Survival Prediction ◽  
Prognostic Signature ◽  
Who Grade ◽  
Cox Analysis

Abstract Background To accurately predict the prognosis of glioma patients. Methods A total of 541 samples from the TCGA cohort, 181 observations from the CGGA database and 91 samples from our cohort were included in our study. Long non-coding RNAs (LncRNAs) associated with glioma WHO grade were evaluated by weighted gene co-expression network analysis (WGCNA). Five lncRNA features were selected out to construct prognostic signatures based on the Cox regression model. Results By weighted gene co-expression network analysis (WGCNA), 14 lncRNAs related to glioma grade were identified. Using univariate and multivariate Cox analysis, five lncRNAs (CYTOR, MIR155HG, LINC00641, AC120036.4 and PWAR6) were selected to develop the prognostic signature. The Kaplan-Meier curve depicted that the patients in high risk group had poor prognosis in all cohorts. The areas under the receiver operating characteristic curve of the signature in predicting the survival of glioma patients at 1, 3, and 5 years were 0.84, 0.92, 0.90 in the CGGA cohort; 0.8, 0.85 and 0.77 in the TCGA set and 0.72, 0.90 and 0.86 in our own cohort. Multivariate Cox analysis demonstrated that the five-lncRNA signature was an independent prognostic indicator in the three sets (CGGA set: HR = 2.002, p < 0.001; TCGA set: HR = 1.243, p = 0.007; Our cohort: HR = 4.457, p = 0.008, respectively). A nomogram including the lncRNAs signature and clinical covariates was constructed and demonstrated high predictive accuracy in predicting 1-, 3- and 5-year survival probability of glioma patients. Conclusion We established a five-lncRNA signature as a potentially reliable tool for survival prediction of glioma patients.

scholarly journals Development of Prognostic Indicator Based on Autophagy-Related lncRNA Analysis in Colon Adenocarcinoma

2020 ◽  
Vol 2020 ◽  
pp. 1-14
Author(s):  
Weige Zhou ◽  
Shijing Zhang ◽  
Hui-biao Li ◽  
Zheyou Cai ◽  
Shuting Tang ◽  
...  
Keyword(s):  
Risk Score ◽  
Noncoding Rna ◽  
Cox Regression ◽  
Risk Group ◽  
Expression Profiles ◽  
Colon Adenocarcinoma ◽  
High Risk Group ◽  
The Cancer Genome Atlas ◽  
Functional Enrichment ◽  
Lasso Regression

There were no systematic researches about autophagy-related long noncoding RNA (lncRNA) signatures to predict the survival of patients with colon adenocarcinoma. It was necessary to set up corresponding autophagy-related lncRNA signatures. The expression profiles of lncRNAs which contained 480 colon adenocarcinoma samples were obtained from The Cancer Genome Atlas (TCGA) database. The coexpression network of lncRNAs and autophagy-related genes was utilized to select autophagy-related lncRNAs. The lncRNAs were further screened using univariate Cox regression. In addition, Lasso regression and multivariate Cox regression were used to develop an autophagy-related lncRNA signature. A risk score based on the signature was established, and Cox regression was used to test whether it was an independent prognostic factor. The functional enrichment of autophagy-related lncRNAs was visualized using Gene Ontology and Kyoto Encyclopedia of Genes and Genomes. Ten prognostic autophagy-related lncRNAs (AC027307.2, AC068580.3, AL138756.1, CD27-AS1, EIF3J-DT, LINC01011, LINC01063, LINC02381, AC073896.3, and SNHG16) were identified to be significantly different, which made up an autophagy-related lncRNA signature. The signature divided patients with colon adenocarcinoma into the low-risk group and the high-risk group. A risk score based on the signature was a significantly independent factor for the patients with colon adenocarcinoma (HR=1.088, 95%CI=1.057−1.120; P<0.001). Additionally, the ten lncRNAs were significantly enriched in autophagy process, metabolism, and tumor classical pathways. In conclusion, the ten autophagy-related lncRNAs and their signature might be molecular biomarkers and therapeutic targets for the patients with colon adenocarcinoma.

scholarly journals A Glycolysis-Based Long Non-coding RNA Signature Accurately Predicts Prognosis in Renal Carcinoma Patients

2021 ◽  
Vol 12 ◽  
Author(s):  
Honghao Cao ◽  
Hang Tong ◽  
Junlong Zhu ◽  
Chenchen Xie ◽  
Zijia Qin ◽  
...  
Keyword(s):  
Clinical Significance ◽  
Risk Score ◽  
Cox Regression ◽  
Risk Group ◽  
Enrichment Analysis ◽  
Roc Curves ◽  
Gene Set Enrichment Analysis ◽  
The Cancer Genome Atlas ◽  
Survival Prediction ◽  
Kaplan Meier

BackgroundThe prognosis of renal cell carcinoma (RCC) varies greatly among different risk groups, and the traditional indicators have limited effect in the identification of risk grade in patients with RCC. The purpose of our study is to explore a glycolysis-based long non-coding RNAs (lncRNAs) signature and verify its potential clinical significance in prognostic prediction of RCC patients.MethodsIn this study, RNA data and clinical information were downloaded from The Cancer Genome Atlas (TCGA) database. Univariate and multivariate cox regression displayed six significantly related lncRNAs (AC124854.1, AC078778.1, EMX2OS, DLGAP1-AS2, AC084876.1, and AC026401.3) which were utilized in construction of risk score by a formula. The accuracy of risk score was verified by a series of statistical methods such as receiver operating characteristic (ROC) curves, nomogram and Kaplan-Meier curves. Its potential clinical significance was excavated by gene enrichment analysis.ResultsKaplan-Meier curves and ROC curves showed reliability of the risk score to predict the prognosis of RCC patients. Stratification analysis indicated that the risk score was independent predictor compare to other traditional clinical parameters. The clinical nomogram showed highly rigorous with index of 0.73 and precisely predicted 1-, 3-, and 5-year survival time of RCC patients. Kyoto Encyclopedia of Genes and Genomes (KEGG) and Gene set enrichment analysis (GSEA) depicted the top ten correlated pathways in both high-risk group and low-risk group. There are 6 lncRNAs and 25 related mRNAs including 36 lncRNA-mRNA links in lncRNA-mRNA co-expression network.ConclusionThis research demonstrated that glycolysis-based lncRNAs possessed an important value in survival prediction of RCC patients, which would be a potential target for future treatment.

scholarly journals Identification of prognostic immune-related lncRNA signature predicting the overall survival for colorectal cancer

2021 ◽  
Author(s):  
Jianxin Li ◽  
Ting Han ◽  
Xin Wang ◽  
Yinchun Wang ◽  
Qingqiang Yang
Keyword(s):  
Colorectal Cancer ◽  
Overall Survival ◽  
High Risk ◽  
Cox Regression ◽  
Risk Group ◽  
Enrichment Analysis ◽  
High Risk Group ◽  
The Cancer Genome Atlas ◽  
Functional Enrichment ◽  
Regression Analyses

Abstract Background Long non-coding RNA (lncRNA) is an important regulator of gene expression and serves fundamental role in immune regulation. The present study aimed to develop a novel immune-related lncRNA signature to accurately assess the prognosis of patients with colorectal cancer (CRC). Methods Transcriptome data and clinical information of patients with CRC were downloaded from The Cancer Genome Atlas (TCGA), and the immune-related mRNAs were extracted from immunomodulatory gene datasets IMMUNE RESPONSE and IMMUNE SYSTEM PROCESS based on the Molecular Signatures Database (MSigDB). Then, the immune-related lncRNAs were identified by a correlation analysis between immune-related mRNAs and lncRNAs. Subsequently, univariate, lasso and multivariate Cox regression were used to identify an immune-related lncRNA signature in training cohort, and the predict ability of the signature was further confirmed in the testing cohort and the entire TCGA cohort. Finally, the lncRNA-mRNA co-expression network was established to explore the biological role of the immune-related lncRNA signature. Results In total, 272 Immune-related lncRNAs were identified, five of which were applied to construct an immune-related lncRNA signature based on univariate, lasso and multivariate Cox regression analyses. The signature divided patients with CRC into low- and high-risk groups, and patients with CRC in high-risk group had poorer overall survival than those in low-risk group. Univariate and multivariate Cox regression analyses confirmed that the signature could be an independent prognostic factor in human CRC. Furthermore, functional enrichment analysis revealed that the immune-related lncRNA signature was significantly enriched in immune process and tumor classical pathways. Conclusions The present study revealed that the novel immune-related lncRNA signature could be exploited as underlying molecular biomarkers and therapeutic targets for the patients with CRC.

scholarly journals Novel Immune-Related Gene Signature for Risk Stratification and Prognosis of Survival in Estrogen Receptor (ER) or Progesterone Receptor (PR) Positive and Human Epidermal Growth Factor Receptor 2 (HER2) Negative Breast Cancer

2021 ◽  
Author(s):  
Wei Wang ◽  
Hongnan Jiang ◽  
Yanrong Gao
Keyword(s):  
Breast Cancer ◽  
Risk Stratification ◽  
Cox Regression ◽  
Risk Group ◽  
Growth Factor Receptor ◽  
Gene Signature ◽  
High Risk Group ◽  
Lasso Regression ◽  
Cox Analysis ◽  
Immune Related Genes

Abstract Background: Although intrinsic molecular subtype has been extensively used, the risk stratification have not been fully elucidated in estrogen receptor (ER) or progesterone receptor (PR) positive and human epidermal growth factor receptor 2 (HER2) negative breast cancer. Methods: RNA transcriptional data from The Cancer Genome Atlas (TCGA), METABRIC and GEO were used. Immune-related genes were obtained from the datasets and literature search. Univariate, lasso regression and multivariate cox regression were employed to identify prognostic immune-related genes and establish the risk signature. Relationships between the risk signature and clinical parameters, tumor-infiltrating immune cell abundances and cancer phenotypes were further evaluated.Results: Noted, 102 immune-related prognostic genes were identified in METABRIC dataset by univariate cox analysis. Consecutively, 7 immune genes (SHMT2, AGA, COL17A1, FLT3, SLC7A2, ATP6AP1 and CCL19) were selected as risk signature by lasso regression and multivariate cox analysis. Its performance was further verified in TCGA,GSE20685 and GSE9195 datasets. Multivariate Cox regression indicated that the risk signature was an independent predictor. The prognostic signature showed significant correlation with intrinsic molecular subtypes, 70-gene signature and tamoxifen resistance signature. The CIBERSORT algorithm revealed that CD4+ memory T cells were significant higher in low-risk group. Conversely, M0-type macrophages were significant higher in high-risk group in both TCGA and METABRIC cohorts, which may have effect on the prognosis. Furthermore, we found that low-risk group may be associated with immune-related pathway and high-risk group was with cell cycle-related pathway, which also showed impact on the prognosis.Conclusion: The present study constructed a robust seven immune-related gene signature and established an effective method in risk stratification and prediction of clinical outcome in ER or PR positive and HER2 negative breast cancer.

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