scholarly journals Identification of an m6A-Related Signature as Biomarker for Hepatocellular Carcinoma Prognosis and Correlates with Sorafenib and Anti-PD-1 Immunotherapy Treatment Response

2021 ◽  
Vol 2021 ◽  
pp. 1-15
Author(s):  
Hongye Jiang ◽  
Gang Ning ◽  
Yensheng Wang ◽  
Weibiao Lv
Keyword(s):  
Hepatocellular Carcinoma ◽  
T Cells ◽  
High Risk ◽  
Lower Risk ◽  
Predictive Accuracy ◽  
Expression Profiles ◽  
Risk Scores ◽  
Sorafenib Treatment ◽  
Risk Patients ◽  
Prediction Of Prognosis

Background. N6-methyladenosine (m6A) modification plays an essential role in diverse key biological processes and may take part in the development and progression of hepatocellular carcinoma (HCC). Here, we systematically analyzed the expression profiles and prognostic values of 13 widely reported m6A modification-related genes in HCC. Methods. The mRNA expression of 13 m6A modification-related genes and clinical parameters of HCC patients were downloaded from TCGA, ICGC, GSE109211, and GSE78220. Univariate and LASSO analyses were used to develop risk signature. Time-dependent ROC was performed to assess the predictive accuracy and sensitivity of risk signature. Results. FTO, YTHDC1, YTHDC2, ALKBH5, KIAA1429, HNRNPC, METTL3, RBM15, YTHDF2, YTHDF1, and WTAP were significantly overexpressed in HCC patients. YTHDF1, HNRNPC, RBM15, METTL3, and YTHDF2 were independent prognostic factors for OS and DFS in HCC patients. Next, a risk signature was also developed and validated with five m6A modification-related genes in TCGA and ICGC HCC cohort. It could effectively stratify HCC patients into high-risk patients with shorter OS and DFS and low-risk patients with longer OS and DFS and showed good predictive efficiency in predicting OS and DFS. Moreover, significantly higher proportions of macrophages M0 cells, neutrophils, and Tregs were found to be enriched in HCC patients with high risk scores, while significantly higher proportions of memory CD4 T cells, gamma delta T cells, and naive B cells were found to be enriched in HCC patients with low scores. Finally, significantly lower risk scores were found at sorafenib treatment responders and anti-PD-1 immunotherapy responders compared to that in nonresponders, and anti-PD-1 immunotherapy-treated patients with lower risk scores had better OS than patients with higher risk scores. Conclusion. A risk signature developed with the expression of 5 m6A-related genes could improve the prediction of prognosis of HCC and correlated with sorafenib treatment and anti-PD-1 immunotherapy response.

scholarly journals Identification and Validation of a Predictive Immune-related Genes Signature for the Prognosis of Cholangiocarcinoma

2020 ◽  
Author(s):  
Rui Zhang ◽  
Chen Chen ◽  
Qi Li ◽  
Jialu Fu ◽  
Dong Zhang ◽  
...  
Keyword(s):  
Gene Expression ◽  
T Cells ◽  
High Risk ◽  
Risk Score ◽  
Risk Model ◽  
Predictive Accuracy ◽  
Expression Profiles ◽  
Gene Expression Profiles ◽  
The Cancer Genome Atlas ◽  
Immune Related Genes

Abstract Background: Immune-related genes (IRGs) play a crucial role in the initiation and progression of cholangiocarcinoma (CCA). However, immune signatures have rarely been used to predict prognosis of CCA. The aim of this study was to construct a novel model for CCA to predict survival based on IRGs expression data.Methods: The gene expression profiles and clinical data of CCA patients from The Cancer Genome Atlas (TCGA) and Gene Expression Omnibus (GEO) database were integrated to establish and validate prognostic IRG signatures. Differentially expressed immune-related genes were screened. Univariate and multivariate Cox analysis were performed to identify prognostic IRGs, and the risk model that predicts outcomes was constructed. Furthermore, receiver operating characteristic (ROC) and Kaplan-Meier curve were plotted to examine predictive accuracy of the model, and a nomogram was constructed based on IRGs signature, combining with other clinical characteristics. Finally, CIBERSORT was used to analyze the association of immune cells infiltration with risk score.Results: We identified that 223 IRGs were significantly dysregulated in patients with CCA, among which five IRGs (AVPR1B, CST4, TDGF1, RAET1E and IL9R) were identified as robust indicators for overall survival (OS), and a prognostic model was built based on the IRGs signature. Meanwhile, patients with high risk had worse OS in training and validation cohort, and the area under the ROC was 0.898 and 0.846, respectively. Nomogram demonstrated that immune risk score contributed much more points than other clinicopathological variables, with a C-index of 0.819 (95% CI, 0.727-0.911). Finally, we found that IRGs signature was positively correlated with the proportion of CD8+ T cells, neurophils and T gamma delta, while negatively with that of CD4+ memory resting T cells.Conclusions: We established and validated an effective five IRGs-based prediction model for CCA, which could accurately classify patients into groups with low and high risk of poor prognosis.

The role of advanced genetic testing in the management of prostate cancer post radical prostatectomy.

2013 ◽  
Vol 31 (15_suppl) ◽  
pp. 5089-5089
Author(s):  
Edward M. Schaeffer ◽  
Ismael A. Vergara ◽  
Anamaria Crisan ◽  
Nicholas Erho ◽  
Mercedeh Ghadessi ◽  
...  
Keyword(s):  
Prostate Cancer ◽  
High Risk ◽  
Radical Prostatectomy ◽  
Expression Profiles ◽  
Transcriptome Profiling ◽  
Good Prognosis ◽  
Risk Scores ◽  
High Risk Prostate Cancer ◽  
Clinicopathologic Factors ◽  
Risk Patients

5089 Background: The genomic classifier (Decipher, GC) is a prospectively validated assay that predicts clinical metastasis post radical prostatectomy (RP) more accurately than standard clinicopathologic factors. While over 70% of high risk patients tested in a previous validation had low GC scores and good prognosis, patients with high GC scores had a cumulative incidence of metastasis over 25% over the study duration. Among men diagnosed with localized prostate cancer, these most at risk patients may derive the greatest benefit from novel therapies. We thus examined differential expression (DE) of druggable genes that may be targeted in this group. Methods: High-density microarray expression profiles of primary FFPE tumor specimens from 764 men treated with RP at the Mayo Clinic (1987-2006) were evaluated. A subset of 323 patients was flagged as high risk of clinical metastasis (mets) by virtue of having GC score ≥ 0.4. Enrichment and identification of DE genes as druggable targets were pursued using DAVID and DrugBank. Results: Median follow-up of patients was 15.1 years. Among the 323 patients with high GC scores, 62% had mets during follow-up.We identified 2,262 genes DE between mets and non-mets, 230 of which are associated with 331 approved pharmaceuticals and 547 experimental agents. These agents included multiple established anti-neoplastic therapies not currently used to treat prostate cancer such as bortezomib, capecitabine, dasatinib, etoposide, gemcitabine, imatinib, irinotecan, pemetrexed and vinblastine. The two most enriched pathways, spliceosome and ubiquitin-mediated proteolysis, have been proposed previously as therapeutic targets in cancer. Conclusions: Advanced genomic testing that includes validated molecular risk scores as well as transcriptome profiling from a single assay may better enable application of directed, multimodal therapy for individual patients with high risk prostate cancer.

scholarly journals Prognosis-Predictive Signature and Nomogram Based on Autophagy-Related Long Non-coding RNAs for Hepatocellular Carcinoma

2020 ◽  
Vol 11 ◽  
Author(s):  
Yu Jia ◽  
Yan Chen ◽  
Jiansheng Liu
Keyword(s):  
Hepatocellular Carcinoma ◽  
High Risk ◽  
Risk Score ◽  
Predictive Ability ◽  
Low Risk ◽  
Tnm Stage ◽  
The Cancer Genome Atlas ◽  
Risk Scores ◽  
Risk Patients ◽  
Non Coding Rnas

Autophagy plays a vital role in hepatocellular carcinoma (HCC) pathogenesis. Long non-coding RNAs (lncRNAs) are considered regulators of autophagy, and the aim of the present study was to investigate the prognostic value of autophagy-related lncRNA (ARlncRNA) and develop a new prognostic signature to predict the 1-year and 3-year overall survival (OS) of HCC patients. Transcriptome and clinical survival information of HCC patients was obtained from The Cancer Genome Atlas database. A set of ARlncRNAs was identified by co-expression analysis, from which seven ARlncRNAs (AC005229.4, AL365203.2, AL117336.3, AC099850.3, ELFN1-AS1, LUCAT1, and AL031985.3) were selected for use as a predictive signature. Risk scores were derived for each patient, who were then divided into high-risk and low-risk groups according to the median risk value. The OS of high-risk patients was significantly lower than that of low-risk patients (P < 0.0001). The 1- and 3-year time-dependent ROC curves were used to evaluate the predictive ability of the risk score (AUC = 0.785 of 1 year, 0.710 of 3 years), and its predictive ability was found to be better than TNM stage. Moreover, the risk score was significantly, linearly related to pathological grade and TNM stage (P < 0.05). Overall, a novel nomogram to predict the 1-year and 3-year OS of HCC patients was developed, which shows good reliability and accuracy, for use in improved treatment decision-making.

scholarly journals Identification and Validation of a Predictive Immune-Related Genes Signature for the Prognosis of Cholangiocarcinoma

2020 ◽  
Author(s):  
Rui Zhang ◽  
Chen Chen ◽  
Qi Li ◽  
Jialu Fu ◽  
Dong Zhang ◽  
...  
Keyword(s):  
Gene Expression ◽  
T Cells ◽  
High Risk ◽  
Risk Score ◽  
Risk Model ◽  
Predictive Accuracy ◽  
Expression Profiles ◽  
Gene Expression Profiles ◽  
The Cancer Genome Atlas ◽  
Immune Related Genes

Abstract Background: Immune-related genes (IRGs) play a crucial role in the initiation and progression of cholangiocarcinoma (CCA). However, immune signatures have rarely been used to predict prognosis of CCA. The aim of this study was to construct a novel model for CCA to predict survival based on IRGs expression data. Methods: The gene expression profiles and clinical data of CCA patients from The Cancer Genome Atlas (TCGA) and Gene Expression Omnibus (GEO) database were integrated to establish and validate prognostic IRG signatures. Differentially expressed immune-related genes were screened. Univariate and multivariate Cox analysis were performed to identify prognostic IRGs, and the risk model that predicts outcomes was constructed. Furthermore, receiver operating characteristic (ROC) and Kaplan-Meier curve were plotted to examine predictive accuracy of the model, and a nomogram was constructed based on IRGs signature, combining with other clinical characteristics. Finally, CIBERSORT was used to analyze the association of immune cells infiltration with risk score. Results: We identified that 223 IRGs were significantly dysregulated in patients with CCA, among which five IRGs (AVPR1B, CST4, TDGF1, RAET1E and IL9R) were identified as robust indicators for overall survival (OS), and a prognostic model was built based on the IRGs signature. Meanwhile, patients with high risk had worse OS in training and validation cohort, and the area under the ROC was 0.898 and 0.846, respectively. Nomogram demonstrated that immune risk score contributed much more points than other clinicopathological variables, with a C-index of 0.819 (95% CI, 0.727-0.911). Finally, we found that IRGs signature was positively correlated with the proportion of CD8+ T cells, neurophils and T gamma delta, while negatively with that of CD4+ memory resting T cells. Conclusions: We established and validated an effective five IRGs-based prediction model for CCA, which could accurately classify patients into groups with low and high risk of poor prognosis.

scholarly journals Allogeneic, Off-the-Shelf, Sars-Cov-2-Specific T Cells to Treat High-Risk Patients with COVID-19

2021 ◽  
Vol 27 (3) ◽  
pp. S348-S349
Author(s):  
Spyridoula Vasileiou ◽  
Manik Kuvalekar ◽  
Aster Workineh ◽  
Ayumi Watanabe ◽  
Yovana Velazquez ◽  
...  
Keyword(s):  
T Cells ◽  
High Risk ◽  
High Risk Patients ◽  
Risk Patients

scholarly journals A Novel Epigenetic Machine Learning Model to Define Risk of Progression for Hepatocellular Carcinoma Patients

2021 ◽  
Vol 22 (3) ◽  
pp. 1075
Author(s):  
Luca Bedon ◽  
Michele Dal Bo ◽  
Monica Mossenta ◽  
Davide Busato ◽  
Giuseppe Toffoli ◽  
...  
Keyword(s):  
Machine Learning ◽  
Hepatocellular Carcinoma ◽  
High Risk ◽  
Progression Free Survival ◽  
Low Risk ◽  
Functional Enrichment ◽  
Free Survival ◽  
High Risk Patients ◽  
Risk Of Progression ◽  
Risk Patients

Although extensive advancements have been made in treatment against hepatocellular carcinoma (HCC), the prognosis of HCC patients remains unsatisfied. It is now clearly established that extensive epigenetic changes act as a driver in human tumors. This study exploits HCC epigenetic deregulation to define a novel prognostic model for monitoring the progression of HCC. We analyzed the genome-wide DNA methylation profile of 374 primary tumor specimens using the Illumina 450 K array data from The Cancer Genome Atlas. We initially used a novel combination of Machine Learning algorithms (Recursive Features Selection, Boruta) to capture early tumor progression features. The subsets of probes obtained were used to train and validate Random Forest models to predict a Progression Free Survival greater or less than 6 months. The model based on 34 epigenetic probes showed the best performance, scoring 0.80 accuracy and 0.51 Matthews Correlation Coefficient on testset. Then, we generated and validated a progression signature based on 4 methylation probes capable of stratifying HCC patients at high and low risk of progression. Survival analysis showed that high risk patients are characterized by a poorer progression free survival compared to low risk patients. Moreover, decision curve analysis confirmed the strength of this predictive tool over conventional clinical parameters. Functional enrichment analysis highlighted that high risk patients differentiated themselves by the upregulation of proliferative pathways. Ultimately, we propose the oncogenic MCM2 gene as a methylation-driven gene of which the representative epigenetic markers could serve both as predictive and prognostic markers. Briefly, our work provides several potential HCC progression epigenetic biomarkers as well as a new signature that may enhance patients surveillance and advances in personalized treatment.

scholarly journals A highly expressed mRNA signature for predicting survival in patients with stage I/II non-small-cell lung cancer after operation

2021 ◽  
Vol 11 (1) ◽  
Author(s):  
Nan Ma ◽  
Lu Si ◽  
Meiling Yang ◽  
Meihua Li ◽  
Zhiyi He
Keyword(s):  
High Risk ◽  
Roc Curve ◽  
Expression Profiles ◽  
Low Risk ◽  
Stage I ◽  
Risk Scores ◽  
Training Set ◽  
Cox Regression Analysis ◽  
Nsclc Patients ◽  
Rna Signature

AbstractThere is an urgent need to identify novel biomarkers that predict the prognosis of patients with NSCLC. In this study,we aim to find out mRNA signature closely related to the prognosis of NSCLC by new algorithm of bioinformatics. Identification of highly expressed mRNA in stage I/II patients with NSCLC was performed with the “Limma” package of R software. Survival analysis of patients with different mRNA expression levels was subsequently calculated by Cox regression analysis, and a multi-RNA signature was obtained by using the training set. Kaplan–Meier estimator, log-rank test and receiver operating characteristic (ROC) curves were used to analyse the predictive ability of the multi-RNA signature. RT-PCR used to verify the expression of the multi-RNA signature, and Westernblot used to verify the expression of proteins related to the multi-RNA signature. We identified fifteen survival-related mRNAs in the training set and classified the patients as high risk or low risk. NSCLC patients with low risk scores had longer disease-free survival than patients with high risk scores. The fifteen-mRNA signature was an independent prognostic factor, as shown by the ROC curve. ROC curve also showed that the combined model of the fifteen-mRNA signature and tumour stage had higher precision than stage alone. The expression of fifteen mRNAs and related proteins were higher in stage II NSCLC than in stage I NSCLC. Multi-gene expression profiles provide a moderate prognostic tool for NSCLC patients with stage I/II disease.

scholarly journals Development of prognosis model for colon cancer based on autophagy-related genes

2020 ◽  
Vol 18 (1) ◽  
Author(s):  
Xu Wang ◽  
Yuanmin Xu ◽  
Ting Li ◽  
Bo Chen ◽  
Wenqi Yang
Keyword(s):  
Colon Cancer ◽  
Regression Analysis ◽  
High Risk ◽  
Cancer Patients ◽  
Risk Score ◽  
Cox Regression ◽  
Patient Survival ◽  
Expression Profiles ◽  
Cox Regression Analysis ◽  
Risk Patients

Abstract Background Autophagy is an orderly catabolic process for degrading and removing unnecessary or dysfunctional cellular components such as proteins and organelles. Although autophagy is known to play an important role in various types of cancer, the effects of autophagy-related genes (ARGs) on colon cancer have not been well studied. Methods Expression profiles from ARGs in 457 colon cancer patients were retrieved from the TCGA database (https://portal.gdc.cancer.gov). Differentially expressed ARGs and ARGs related to overall patient survival were identified. Cox proportional-hazard models were used to investigate the association between ARG expression profiles and patient prognosis. Results Twenty ARGs were significantly associated with the overall survival of colon cancer patients. Five of these ARGs had a mutation rate ≥ 3%. Patients were divided into high-risk and low-risk groups based on Cox regression analysis of 8 ARGs. Low-risk patients had a significantly longer survival time than high-risk patients (p < 0.001). Univariate and multivariate Cox regression analysis showed that the resulting risk score, which was associated with infiltration depth and metastasis, could be an independent predictor of patient survival. A nomogram was established to predict 1-, 3-, and 5-year survival of colon cancer patients based on 5 independent prognosis factors, including the risk score. The prognostic nomogram with online webserver was more effective and convenient to provide information for researchers and clinicians. Conclusion The 8 ARGs can be used to predict the prognosis of patients and provide information for their individualized treatment.

scholarly journals Selective depletion of bone marrow T lymphocytes with anti-CD5 monoclonal antibodies: effective prophylaxis for graft-versus-host disease in patients with hematologic malignancies

Blood ◽  
1991 ◽  
Vol 78 (8) ◽  
pp. 2139-2149 ◽  
Author(s):  
JH Antin ◽  
BE Bierer ◽  
BR Smith ◽  
J Ferrara ◽  
EC Guinan ◽  
...  
Keyword(s):  
Bone Marrow ◽  
T Cells ◽  
High Risk ◽  
Graft Versus Host Disease ◽  
Graft Failure ◽  
Hematologic Malignancies ◽  
Host Disease ◽  
Graft Versus Host ◽  
High Risk Patients ◽  
Risk Patients

Seventy-one patients with hematologic malignancies received bone marrow from a histocompatible sibling (n = 48) or a partially matched relative (n = 23) that had been depleted of CD5+ T cells with either an anti-CD5 mooclonal antibody (MoAb) plus complement (anti-Leu1 + C) or an anti- CD5 MoAb conjugated to ricin A chain (ST1 immunotoxin [ST1-IT]). These patients received intensive chemoradiotherapy consisting of cytosine arabinoside, cyclophosphamide, and fractionated total body irradiation. Both anti-Leu1 + C and ST1-IT ex vivo treatments effectively depleted bone marrow of T cells (97% and 95%, respectively). Overall, primary and late graft failure each occurred in 4% of evaluable patients. The diagnosis of myelodysplasia was a significant risk factor for graft failure (P less than .001), and if myelodysplastic patients were excluded, there were no graft failures in major histocompatibility complex (MHC)-matched patients and 2 of 23 (8.7%) in MHC-mismatched patients. The actuarial risk of grade 2 to 4 acute graft-versus-host disease (GVHD) was 23% in MHC-matched patients and 50% in MHC- mismatched patients. In MHC-matched patients, acute GVHD tended to be mild and treatable with corticosteroids. Chronic GVHD was observed in 6 of 36 (17%) MHC-matched patients and none of 11 MHC-mismatched patients. There were no deaths attributable to GVHD in the MHC-matched group. Epstein-Barr virus-associated lymphoproliferative disorders were observed in 3 of 23 MHC-mismatched patients. The actuarial event-free survival was 38% in the MHC-matched patients versus 21% in the MHC- mismatched patients. However, if outcome is analyzed by risk of relapse, low-risk patients had a 62% actuarial survival compared with 11% in high-risk patients. These data indicate that the use of anti-CD5 MoAbs can effectively control GVHD in histocompatible patients, and that additional strategies are required in MHC-mismatched and high-risk patients.

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