scholarly journals Immune-Related Genes Are Prognostic Markers for Prostate Cancer Recurrence

2021 ◽  
Vol 12 ◽  
Author(s):  
Min Fu ◽  
Qiang Wang ◽  
Hanbo Wang ◽  
Yun Dai ◽  
Jin Wang ◽  
...  
Keyword(s):  
Prostate Cancer ◽  
High Risk ◽  
Cox Regression ◽  
Risk Group ◽  
Gene Mutations ◽  
Risk Groups ◽  
High Risk Group ◽  
Prognosis Model ◽  
Immune Related Genes ◽  
Risk Prognosis

BackgroundProstate cancer (PCa) is an immune-responsive disease. The current study sought to explore a robust immune-related prognostic gene signature for PCa.MethodsData were retrieved from the tumor Genome Atlas (TCGA) database and GSE46602 database for performing the least absolute shrinkage and selection operator (LASSO) cox regression model analysis. Immune related genes (IRGs) data were retrieved from ImmPort database.ResultsThe weighted gene co-expression network analysis (WGCNA) showed that nine functional modules are correlated with the biochemical recurrence of PCa, including 259 IRGs. Univariate regression analysis and survival analysis identified 35 IRGs correlated with the prognosis of PCa. LASSO Cox regression model analysis was used to construct a risk prognosis model comprising 18 IRGs. Multivariate regression analysis showed that risk score was an independent predictor of the prognosis of PCa. A nomogram comprising a combination of this model and other clinical features showed good prediction accuracy in predicting the prognosis of PCa. Further analysis showed that different risk groups harbored different gene mutations, differential transcriptome expression and different immune infiltration levels. Patients in the high-risk group exhibited more gene mutations compared with those in the low-risk group. Patients in the high-risk groups showed high-frequency mutations in TP53. Immune infiltration analysis showed that M2 macrophages were significantly enriched in the high-risk group implying that it affected prognosis of PCa patients. In addition, immunostimulatory genes were differentially expressed in the high-risk group compared with the low-risk group. BIRC5, as an immune-related gene in the prediction model, was up-regulated in 87.5% of prostate cancer tissues. Knockdown of BIRC5 can inhibit cell proliferation and migration.ConclusionIn summary, a risk prognosis model based on IGRs was developed. A nomogram comprising a combination of this model and other clinical features showed good accuracy in predicting the prognosis of PCa. This model provides a basis for personalized treatment of PCa and can help clinicians in making effective treatment decisions.

Reduced Intensity Conditioning with Thiotepa and Fludarabine for Allogeneic Transplantation: Evidence for Low Toxicity and Long-Lasting Disease Control in MDS with Low/Intermediate-1 IPSS Score and in AML from MDS in Complete Remission.

Blood ◽  
2008 ◽  
Vol 112 (11) ◽  
pp. 3285-3285
Author(s):  
Emilio Paolo Alessandrino ◽  
Luca Malcovati ◽  
Giorgio La Nasa ◽  
Paolo de Fabritiis ◽  
Massimo Bernardi ◽  
...  
Keyword(s):  
High Risk ◽  
Complete Remission ◽  
Cox Regression ◽  
Risk Group ◽  
Prognostic Score ◽  
Conditioning Regimen ◽  
Disease Status ◽  
Risk Groups ◽  
High Risk Group ◽  
Valvular Stenosis

Abstract This study investigated Thiotepa (TT) and Fludarabine (Fluda) as a preparative regimen for allogeneic peripheral stem cell transplantation in patients with myelodysplastic syndrome (MDS) or acute leukemia from MDS (MDS-AML) older than 50 or with comorbidities contraindicating standard conditioning. Patients were prepared with TT, given over 3 hours as an i.v. infusion at a dose of 10 mg/kg over two days (day -8 and day -7) and Fluda at the dose of 125 mg/m2 i.v. over five days ( from day -7 to day -3). Fresh or cryopreserved allogeneic peripheral stem cells were infused on day 0 or +1. Graft-versus-Host Disease (GvHD) prophylaxis consisted of cyclosporine A (CyA) at the dose of 1.5 mg/kg day as a continuous iv infusion from day -5 until engraftment. The CyA was then administered orally at the dose of 3 mg/kg twice a day. Doses were adjusted to maintain plasma level concentrations between 150–350 mg/dL. From day +60, in the absence of acute GvHD, the CyA was tapered down by 20% every 2 weeks until withdrawal. In addition, patients received methotrexate 10 mg/m2 on day +1, and 8 mg/m2 on days + 3, +6 and +11 after transplantation. At the time of transplantation, patients were classified in two risk groups (low vs high risk) according to IPSS score (low/intermediate-1 vs. intermediate-2/high) for MDS patients, and disease status (CR vs. not CR) for MDS-AML. Kaplan-Meier survival analysis was carried out to compare Overall Survival (OS), Transplant-Related Mortality (TRM) and probability of relapse. Fifty patients (29 males, 21 females) entered the study; the median age was 54 years (range 38–71). Sixteen MDS patients had a low/intermediate 1 score according to the International Prognostic Score System (IPSS), 16 had an intermediate 2/high IPSS score, 18 had MDS-AML. Thirty patients underwent transplantation as front-line therapy, 20 received one or more cycles of chemotherapy before transplant. Among the latter, nine with MDS-AML were in complete remission at the time of their transplant, while four were in a partial remission. The interval from diagnosis to transplantation ranged from 1 to 52 months (median value 11 months). Contraindications to a standard conditioning regimen were liver disease, hypertrophic cardiomyopathy secondary to hypertension or valvular stenosis, cardiac arrhythmia, diabetes mellitus, hypothyroidism, previous CNS bleeding, and a history of sepsis. All but one patient achieved engraftment, with full donor chimerism by day +30. Patients were followed up for a median time of 21 months (range 0.2–87). TRM at 1 and 2 years after transplantation was 25% and 33%; the 5-year probability of relapse was 27%. Twenty-six patients are alive in complete remission, and the 5-year OS is 50%. The 5-year OS was 73% and 28% in low- and high-risk patients respectively (p=0.002). TRM at 1 and 2 years after transplantation was 13% and 21% in the low-risk group and 39% and 45% in the high-risk group (p=0.046); the 5-year probability of relapse was 10% and 50% in the low- and high-risk group respectively (p=0.015). In a multivariate Cox regression, risk group retained a borderline significance (HR=2.6, p=0.07) when adjusted by age at transplantation (p=0.03) and interval from diagnosis to transplant (n.s.). The combination of Thiotepa and Fludarabine is an effective and well-tolerated conditioning regimen in patients with MDS or MDS-AML who are poor candidates for standard myeloablative transplantation, particularly in MDS patients with low/intermediate-1 IPSS score and MDS-AML patients in CR.

scholarly journals Discovery and validation of immune- related long non-coding RNA biomarkers associated with prognosis in hepatocellular carcinoma

2020 ◽  
Author(s):  
Li Liu ◽  
She Tian ◽  
Zhu Li ◽  
Yongjun Gong ◽  
Hao Zhang
Keyword(s):  
Hepatocellular Carcinoma ◽  
High Risk ◽  
Cox Regression ◽  
Risk Group ◽  
Survival Curve ◽  
Principal Component ◽  
High Risk Group ◽  
Low Risk ◽  
Cox Regression Analysis ◽  
Immune Related Genes

Abstract Background : Hepatocellular carcinoma (HCC) is one of the most common clinical malignant tumors, resulting in high mortality and poor prognosis. Studies have found that LncRNA plays an important role in the onset, metastasis and recurrence of hepatocellular carcinoma. The immune system plays a vital role in the development, progression, metastasis and recurrence of cancer. Therefore, immune-related lncRNA can be used as a novel biomarker to predict the prognosis of hepatocellular carcinoma. Methods : The transcriptome data and clinical data of HCC patients were obtained by using The Cancer Genome Atlas-Liver Hepatocellular Carcinoma (TCGA‑LIHC), and immune-related genes were extracted from the Molecular Signatures Database (IMMUNE RESPONSE M19817 and IMMUNE SYSTEM PROCESS M13664). By constructing the co-expression network and Cox regression analysis, 13 immune-lncRNAs was identified to predict the prognosis of HCC patients. Patients were divided into high risk group and low risk group by using the risk score formula, and the difference in overall survival (OS) between the two groups was reflected by Kaplan-Meier survival curve. The time - dependent receiver operating characteristics (ROC) analysis and principal component analysis (PCA) were used to evaluate 13 immune -lncRNAs signature. Results : Through TCGA - LIHC extracted from 343 cases of patients with hepatocellular carcinoma RNA - Seq data and clinical data, 331 immune-related genes were extracted from the Molecular Signatures Database , co-expression networks and Cox regression analysis were constructed, 13 immune-lncRNAs signature was identified as biomarkers to predict the prognosis of patients. At the same time using the risk score median divided the patients into high risk and low risk groups, and through the Kaplan-Meier survival curve analysis found that high-risk group of patients' overall survival (OS) less low risk group of patients. The AUC value of the ROC curve is 0.828, and principal component analysis (PCA) results showed that patients could be clearly divided into two parts by immune-lncRNAs, which provided evidence for the use of 13 immune-lncRNAs signature as prognostic markers. Conclusion : Our study identified 13 immune-lncRNAs signature that can effectively predict the prognosis of HCC patients, which may be a new prognostic indicator for predicting clinical outcomes.

A prediction rule for the development of arthritis in seropositive arthralgia patients

2012 ◽  
Vol 72 (12) ◽  
pp. 1920-1926 ◽  
Author(s):  
Lotte Arwen van de Stadt ◽  
Birgit I Witte ◽  
Wouter H Bos ◽  
Dirkjan van Schaardenburg
Keyword(s):  
High Risk ◽  
Cox Regression ◽  
Risk Group ◽  
Area Under The Curve ◽  
Prediction Rule ◽  
Risk Groups ◽  
High Risk Group ◽  
Visual Analogue Scale Pain ◽  
Duration Of Symptoms ◽  
Clinical Arthritis

ObjectiveTo predict the development of arthritis in anticyclic citrullinated peptide antibodies and/or IgM rheumatoid factor positive (seropositive) arthralgia patients.MethodsA prediction rule was developed using a prospective cohort of 374 seropositive arthralgia patients, followed for the development of arthritis. The model was created with backward stepwise Cox regression with 18 variables.Results131 patients (35%) developed arthritis after a median of 12 months. The prediction model consisted of nine variables: Rheumatoid Arthritis in a first degree family member, alcohol non-use, duration of symptoms <12 months, presence of intermittent symptoms, arthralgia in upper and lower extremities, visual analogue scale pain ≥50, presence of morning stiffness ≥1 h, history of swollen joints as reported by the patient and antibody status. A simplified prediction rule was made ranging from 0 to 13 points. The area under the curve value (95% CI) of this prediction rule was 0.82 (0.75–0.89) after 5 years. Harrell's C (95% CI) was 0.78 (0.73–0.84). Patients could be categorised in three risk groups: low (0–4 points), intermediate (5–6 points) and high risk (7–13 points). With the low risk group as a reference, the intermediate risk group had a hazard ratio (HR; 95% CI) of 4.52 (2.42–8.77) and the high risk group had a HR of 14.86 (8.40–28.32).ConclusionsIn patients presenting with seropositive arthralgia, the risk of developing arthritis can be predicted. The prediction rule that was made in this patient group can help (1) to inform patients and (2) to select high-risk patients for intervention studies before clinical arthritis occurs.

scholarly journals Prognostic Value of Immune-Related genes in the Tumor Microenvironment of Glioma - Analyzed by Integrated Bioinformatics

2021 ◽  
Author(s):  
Yali Zhong ◽  
Xiaobin Luo ◽  
Fubing Yang ◽  
Xinling Song
Keyword(s):  
T Cells ◽  
High Risk ◽  
Risk Prediction ◽  
Immune Cell ◽  
Risk Group ◽  
Risk Groups ◽  
High Risk Group ◽  
Low Risk ◽  
Lasso Regression ◽  
Immune Related Genes

Abstract Object: Immune related genes play an important role in the process of tumor genesis and development. Therefore, we aim to find the Immune genes which are related to the prognosis of glioma patients, and to explore the infiltration of Immune cells in glioma microenvironment. Methods We downloaded the data of the glioma samples from the CGGA database, and performed batch correction to screen the primary glioma samples for subsequent analysis. Then the ESTIMATE algorithm was used to deal with the Stromal scores and Immune scores of the primary glioma samples, and the difference was analyzed. Then the common Immune related genes (IRGs) were obtained by intersecting with the Immune genes in the ImmPort database. Moreover, we used common IRGs to construct protein-protein interaction (PPI) networks, from which we screened the top 30 genes with high connectivity, and Lasso regression was used to screen the IRGs. Lastly, we obtained the combined genes, which were overlapped both in the top 30 high-connection genes and Lasso regression genes. The final genes were used to construct COX risk prediction models. The accuracy of the model were verified by the TCGA glioma data, and the model genes were analyzed for Immune-related pathways, as well as the Hallmark and KEGG enrichment. Additionally, we used CIBERSOFT algorithm to estimate the Immune cell content of the samples, and analyzed the differences, correlations and survival of the Immune cells in high and low risk groups. Results Firstly, a total of 117 IRGs were obtained from the gene sets, which were overlapped in the data of Stromal score, Immune score and ImmPort database. Secondly, the top 30 genes were selected after the PPI network, and another 26 genes were screened out after the Lasso regression algorithm. And then, six coexist IRGs were obtained from the intersecting sets. Furthermore, the COX risk prediction model was constructed and tested, showing that the overall survival rate of the high-risk group was about 50% of that of the low-risk group. We observed that the high-risk group were enriched in Immune response and Immune process. Most importantly, in KEGG pathways, the high-risk groups were mainly enriched in p53 signaling pathway, JAK-STAT signaling pathway, pathways in cancer and cell cycle. By estimating the Immune cell contents, we also found that the Immune cell Plasma cells, T cells CD8, T cells CD4 naïve, T cells regulatory (Tregs), Macrophages M0 and Neutrophils were higher in high-risk groups, when compared to the low-risk group, with significant difference. Finally, the correlation analysis showed that the degree of Immune infiltration in high-risk groups was related to T cells regulatory (Tregs), Macrophages M0 and Neutrophils. Conclusion A COX risk prediction model of 6 genes was successfully constructed, which was enriched in Immune-related pathways. Meanwhile, survival analysis and TCGA data validation revealed significant differences in the model genes in the overall survival of the glioma patients, and the degree of Immune infiltration in the model was associated with T cells regulatory (Tregs), Macrophages M0 and Neutrophils.

scholarly journals Utility of radiomics for predicting patient survival in hepatocellular carcinoma with portal vein tumor thrombosis treated with stereotactic body radiotherapy

2020 ◽  
Author(s):  
Kui Wu ◽  
Yongjie Shui ◽  
Wenzheng Sun ◽  
Sheng Lin ◽  
Haowen Pang
Keyword(s):  
High Risk ◽  
Clinical Characteristics ◽  
Cox Regression ◽  
Patient Survival ◽  
Risk Group ◽  
Characteristic Curve ◽  
Area Under The Curve ◽  
Risk Groups ◽  
High Risk Group ◽  
Low Risk

Abstract Objective This study aimed to develop and validate the combination of radiomic features and clinical characteristics that can predict patient survival in HCC with PVTT treated with SBRT. Materials and Methods The prediction model was developed in a primary cohort of 70 patients with HCC and PVTT treated with SBRT, using data acquired between December 2015 and June 2017. The radiomic features were extracted from computed tomography (CT) scans. A least absolute shrinkage and selection operator regression model was used to build the radiomic feature. Multivariate Cox-regression hazard models were created for analyzing survival outcomes and the radiomic features and clinical characteristics were presented with a nomogram. The area under the curve (AUC) of the receiver operating characteristic curve was used to evaluate the model. Participants were divided into a high-risk group and a low-risk group based on the radiomic features. Results A total of seven radiomic features and five clinical characteristics were extracted for survival analysis. A combination of the radiomic features and clinical characteristics resulted in better performance for the estimation of overall survival (OS) [AUC = 0.859 (CI: 0.770–0.948)] than that with clinical characteristics alone [AUC = 0.761 (CI: 0.641–0.881)]. These patients were divided into high-risk and low-risk groups according to the radiomic features. Conclusion This study demonstrated that a nomogram of combined radiomic features and clinical characteristics can be conveniently used to facilitate individualized preoperative prediction of OS in patients with HCC with PVTT before SBRT.

Construction of a Prognostic Model Related to Ferroptosis and Iron-Metabolism and the Relationship with the Immune Microenvironment of Gastric Cancer

2021 ◽  
Author(s):  
Fang Wen ◽  
Xiaoxue Chen ◽  
Wenjie Huang ◽  
Shuai Ruan ◽  
Suping Gu ◽  
...  
Keyword(s):  
Gastric Cancer ◽  
High Risk ◽  
Iron Metabolism ◽  
Risk Model ◽  
Cox Regression ◽  
Risk Group ◽  
Risk Groups ◽  
High Risk Group ◽  
Low Risk ◽  
Immune Microenvironment

Abstract Background: The diagnosis rate and mortality of gastric cancer (GC) are among the highest in the global, so it is of great significance to predict the survival time of GC patients. Ferroptosis and iron-metabolism make a critical impact on tumor development and are closely linked to the treatment of cancer and the prognosis of patients. However, the predictive value of the genes involved in ferroptosis and iron-metabolism in GC and their effects on immune microenvironment remain to be further clarified.Methods: In this study, the RNA sequence information and general clinical indicators of GC patients were acquired from the public databases. We first systematically screen out 134 DEGs and 13 PRGs related to ferroptosis and iron-metabolism. Then, we identified six PRDEGs (GLS2, MTF1, SLC1A5, SP1, NOX4, and ZFP36) based on the LASSO-penalized Cox regression analysis. The 6-gene prognostic risk model was established in the TCGA cohort and the GC patients were separated into the high- and the low-risk groups through the risk score median value. GEO cohort was used for verification. The expression of PRDEGs was verified by quantitative QPCR.Results: Our study demonstrated that patients in the low-risk group had a higher survival probability compared with those in high-risk group. In addition, univariate and multivariate Cox regression analyses confirmed that the risk score was an independent prediction parameter. The ROC curve analysis and nomogram manifested that the risk model had the high predictive ability and was more sensitive than general clinical features. Furthermore, compared with the high-risk group, the low-risk group had higher TMB and a longer 5-year survival period. In the immune microenvironment of GC, there were also differences in immune function and highly infiltrated immune cells between the two risk groups.Conclusions: The prognostic risk model based on the six genes associated with ferroptosis and iron-metabolism has a good performance for predicting the prognosis of patients with GC. The treatment of cancer by inducing tumor ferroptosis or mediating tumor iron-metabolism, especially combined with immunotherapy, provides a new possibility for individualized treatment of GC patients.

scholarly journals A Novel Ferroptosis-related Gene Signature for Prognosis Prediction in Glioma

2021 ◽  
Author(s):  
Tian Lan ◽  
Die Wu ◽  
Wei Quan ◽  
Donghu Yu ◽  
Sheng Li ◽  
...  
Keyword(s):  
Overall Survival ◽  
High Risk ◽  
Cox Regression ◽  
Risk Group ◽  
Risk Groups ◽  
Gene Signature ◽  
High Risk Group ◽  
Differential Analysis ◽  
Related Gene ◽  
Cox Regression Analysis

Abstract Background: Glioma is a fatal brain tumor characterized by invasive nature, rapidly proliferation and tumor recurrence. Despite aggressive surgical resection followed by concurrent radiotherapy and chemotherapy, the overall survival (OS) of Glioma patients remains poor. Ferroptosis is a unique modality to regulate programmed cell death and associated with multiple steps of tumorigenesis of a variety of tumors.Methods: In this study, ferroptosis-related genes model was identified by differential analysis and Cox regression analysis. GO, KEGG and GSVA analysis were used to detect the potential biological functions and signaling pathway. The infiltration of immune cells was quantified by Cibersort.Results: The patients’ samples are stratified into two risk groups based on 4-gene signature. High-risk group has poorer overall survival. The results of functional analysis indicated that the extracellular matrix-related biologic functions and pathways were enriched in high-risk group, and that the infiltration of immunocytes is different in two groups.Conclusion: In summary, a novel ferroptosis-related gene signature can be used for prognostic prediction in glioma. The filtered genes related to ferroptosis in clinical could be a potential extra method to assess glioma patients’ prognosis and therapeutic.

scholarly journals Immune-Related lncRNA Signature for Predicting the Immune Landscape of Head and Neck Squamous Cell Carcinoma

2021 ◽  
Vol 8 ◽  
Author(s):  
Ji Yin ◽  
Xiaohui Li ◽  
Caifeng Lv ◽  
Xian He ◽  
Xiaoqin Luo ◽  
...  
Keyword(s):  
Squamous Cell Carcinoma ◽  
Regression Analysis ◽  
High Risk ◽  
Cell Carcinoma ◽  
Cox Regression ◽  
Risk Group ◽  
Risk Groups ◽  
High Risk Group ◽  
Survival Prediction ◽  
Cox Regression Analysis

Background: Long non-coding RNA (lncRNA) plays a significant role in the development, establishment, and progression of head and neck squamous cell carcinoma (HNSCC). This article aims to develop an immune-related lncRNA (irlncRNA) model, regardless of expression levels, for risk assessment and prognosis prediction in HNSCC patients.Methods: We obtained clinical data and corresponding full transcriptome expression of HNSCC patients from TCGA, downloaded GTF files to distinguish lncRNAs from Ensembl, discerned irlncRNAs based on co-expression analysis, distinguished differentially expressed irlncRNAs (DEirlncRNAs), and paired these DEirlncRNAs. Univariate Cox regression analysis, LASSO regression analysis, and stepwise multivariate Cox regression analysis were then performed to screen lncRNA pairs, calculate the risk coefficient, and establish a prognosis model. Finally, the predictive power of this model was validated through the AUC and the ROC curves, and the AIC values of each point on the five-year ROC curve were calculated to select the maximum inflection point, which was applied as a cut-off point to divide patients into low- or high-risk groups. Based on this methodology, we were able to more effectively differentiate between these groups in terms of survival, clinico-pathological characteristics, tumor immune infiltrating status, chemotherapeutics sensitivity, and immunosuppressive molecules.Results: A 13-irlncRNA-pair signature was built, and the ROC analysis demonstrated high sensitivity and specificity of this signature for survival prediction. The Kaplan–Meier analysis indicated that the high-risk group had a significantly shorter survival rate than the low-risk group, and the chi-squared test certified that the signature was highly related to survival status, clinical stage, T stage, and N stage. Additionally, the signature was further proven to be an independent prognostic risk factor via the Cox regression analyses, and immune infiltrating analyses showed that the high-risk group had significant negative relationships with various immune infiltrations. Finally, the chemotherapeutics sensitivity and the expression level of molecular markers were also significantly different between high- and low-risk groups.Conclusion: The signature established by paring irlncRNAs, with regard to specific expression levels, can be utilized for survival prediction and to guide clinical therapy in HNSCC.

scholarly journals Identification of a novel autophagy signature for predicting survival in patients with lung adenocarcinoma

PeerJ ◽  
2021 ◽  
Vol 9 ◽  
pp. e11074
Author(s):  
Jin Duan ◽  
Youming Lei ◽  
Guoli Lv ◽  
Yinqiang Liu ◽  
Wei Zhao ◽  
...  
Keyword(s):  
Gene Expression ◽  
High Risk ◽  
Immune Cell ◽  
Cox Regression ◽  
Risk Group ◽  
Risk Groups ◽  
High Risk Group ◽  
Low Risk ◽  
Cell Analysis ◽  
Prognostic Signature

Background Lung adenocarcinoma (LUAD) is the most commonhistological lung cancer subtype, with an overall five-year survivalrate of only 17%. In this study, we aimed to identify autophagy-related genes (ARGs) and develop an LUAD prognostic signature. Methods In this study, we obtained ARGs from three databases and downloaded gene expression profiles from The Cancer Genome Atlas (TCGA) and Gene Expression Omnibus (GEO) database. We used TCGA-LUAD (n = 490) for a training and testing dataset, and GSE50081 (n = 127) as the external validation dataset.The least absolute shrinkage and selection operator (LASSO) Cox and multivariate Cox regression models were used to generate an autophagy-related signature. We performed gene set enrichment analysis (GSEA) and immune cell analysis between the high- and low-risk groups. A nomogram was built to guide the individual treatment for LUAD patients. Results We identified a total of 83 differentially expressed ARGs (DEARGs) from the TCGA-LUAD dataset, including 33 upregulated DEARGs and 50 downregulated DEARGs, both with thresholds of adjusted P < 0.05 and |Fold change| > 1.5. Using LASSO and multivariate Cox regression analyses, we identified 10 ARGs that we used to build a prognostic signature with areas under the curve (AUCs) of 0.705, 0.715, and 0.778 at 1, 3, and 5 years, respectively. Using the risk score formula, the LUAD patients were divided into low- or high-risk groups. Our GSEA results suggested that the low-risk group were enriched in metabolism and immune-related pathways, while the high-risk group was involved in tumorigenesis and tumor progression pathways. Immune cell analysis revealed that, when compared to the high-risk group, the low-risk group had a lower cell fraction of M0- and M1- macrophages, and higher CD4 and PD-L1 expression levels. Conclusion Our identified robust signature may provide novel insight into underlying autophagy mechanisms as well as therapeutic strategies for LUAD treatment.

scholarly journals Development and validation of a six-RNA binding proteins prognostic signature and candidate drugs for prostate cancer

2020 ◽  
Author(s):  
Lei Gao ◽  
Jialin Meng ◽  
Yong Zhang ◽  
Junfei Gu ◽  
Zhenwei Han ◽  
...  
Keyword(s):  
Prostate Cancer ◽  
High Risk ◽  
Binding Proteins ◽  
Rna Binding ◽  
Cox Regression ◽  
Rna Binding Proteins ◽  
Risk Group ◽  
Life Quality ◽  
High Risk Group ◽  
Prognostic Signature

AbstractThe dysregulation of RNA binding proteins (RBPs) play critical roles in the progression of several cancers. However, the overall functions of RBPs in prostate cancer (PCa) remain poorly understood. Therefore, we first identified 144 differentially expressed RBPs in tumors compared to normal tissues based on the TCGA dataset. Next, six RBP genes (MSI1, MBNL2, LENG9, REXO2, RNASE1, PABPC1L) were screened out as prognosis hub genes by univariate, LASSO and multivariate Cox regression and used to establish the prognostic signature. Further analysis indicated that high risk group was significantly associated with poor RFS, which was validated in the MSKCC cohort. Besides, patients in high risk group was closely associated with dysregulation of DNA damage repair pathway, copy number alteration, tumor burden mutation and low-respond to cisplatin (P < 0.001), bicalutamide (P < 0.001). Finally, three drugs (ribavirin, carmustine, carbenoxolone) were predicted using Connectivity Map. In summary, we identified a six-RBP gene signature and three candidate drugs against PCa, which may promote the individualized treatment and further improve the life quality of PCa patients.

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