Development and validation of a six-gene signature for predicting prognosis in prostate cancer patients with lymph node metastasis.

2020 ◽  
Vol 38 (15_suppl) ◽  
pp. e17524-e17524
Author(s):  
Jinan Guo ◽  
Wenzhuan Xie ◽  
Mengli Huang ◽  
Chan Gao
Keyword(s):  
Lymph Node ◽  
High Risk ◽  
Lymph Node Metastasis ◽  
Cox Regression ◽  
Risk Group ◽  
Gene Signature ◽  
High Risk Group ◽  
Wild Type ◽  
Cox Regression Analysis ◽  
Node Metastasis

e17524 Background: Prostate cancer (PCa) patients with lymph node metastasis (LNM) always exhibit poor clinical outcomes. A gene signature that could predict survival in these patients would allow for earlier detection of mortality risk and will also guide individualized therapy. Methods: A prediction model was developed using a public cohort consisting of 623 patients with clinicopathologically confirmed PCa. Data were gathered from cBioPortal and UCSC Xena. Genes expressed differentially in patients with lymph node metastasis versus those without lymph node metastasis were identified. Uni-variate Cox regression analysis and LASSO Cox regression were applied to build a prediction model. Time-dependent receiver operating characteristic (ROC) and Kaplan-Meier curves were used to assess the prognostic capacity of the model, followed by external validation using the MSKCC dataset from cBioPortal. Gene Set Enrichment Analysis (GSEA) was performed to further understand the underlying molecular mechanisms. Results: We identified a six-gene signature (covering GSDMB, SSTR1, MX1, CCBE1, MYBPC1, and FAM3D) that could effectively identify a high-risk subset of PCa patients. ROC analysis indicated that the signature had a good performance (AUC > 0.7) in survival prediction in both the training and the testing/validation cohorts. Cox regression analysis showed that the six-gene signature could independently predict disease-free survival (DFS) as well, although with lower predictive power. Subgroup analyses showed that signature-based risk score may serve as a promising marker to predict DFS in different subgroups, including stage T2 (HR = 0.12, p < 0.001), stage T3 (HR = 0.29, p < 0.001), TP53-wild-type (HR = 0.22, p < 0.001), TP53-mutated (HR = 0.07, p < 0001), AR pathways-wild-type (HR = 0.2, p < 0.001) and AR pathways-mutated(HR = 0.16, p = 0.0419). The performance of the six-gene signature in LNM+ was stable for stratifying the patients according to risk of deatch (HR = 0.23, p = 0.0333). Moreover, GSEA revealed distinct pathway enrichment features in the different risk groups, where pathways related to DNA repair were more prominently enriched in the high-risk group while the low-risk group had higher enrichment of androgen response. Conclusions: We developed a robust six-gene signature that can effectively classify PCa patients into groups with low- and high-risk group, which may help select high-risk patients who require more aggressive adjuvant target therapy or immune therapy.

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.

A validation study of a scoring system to estimate the risk of lymph node metastasis for patients with endometrial carcinoma for tailoring the indication of lymphadenectomy

2006 ◽  
Vol 24 (18_suppl) ◽  
pp. 5040-5040
Author(s):  
Y. Todo ◽  
Y. Ebina ◽  
H. Watari ◽  
N. Sakuragi
Keyword(s):  
Lymph Node ◽  
High Risk ◽  
Lymph Node Metastasis ◽  
Endometrial Carcinoma ◽  
Validation Study ◽  
Risk Group ◽  
Tumor Grade ◽  
High Risk Group ◽  
Volume Index ◽  
Node Metastasis

5040 Background: The present standard treatment for cases with endometrial cancer is surgical staging including lymphadenectomy. Elimination of lymphadenectomy will not be approved of unless strict condition are met. Our aim is to verify whether a preoperative scoring system to estimate the risk of lymph node metastasis (LNM) in endometrial carcinoma is clinically useful for tailoring the indication of lymphadenectomy. Methods: This study was carried out on 211 patients with endometrial carcinoma for whom volume index, serum CA125 level, tumor grade/histology were preoperatively confirmed. LNM score was set up using these three risk factors as reported in our previous study (Am J Obstet Gynecol 2003). We analyzed whether these factors remain still valid in a different cohort of patients. Based on the LNM score before a validation study was started, the estimated rate of lymph node metastasis (para-aortic lymph node metastasis) in a low risk group was 3.4% (0.0%), an intermediate group 7.7% (5.8%), a high risk group 44.4% (30.6%) and an extremely high risk group 70.0% (50.0%). Results: Volume index, serum CA125 level, and tumor grade/histology, were found to be independent risk factors for LNM in the cohort of this validation study. The actual rate of lymph node metastasis (para-aortic lymph node metastasis) in a low risk group was 3.2% (1.0%), an intermediate group 15.3% (11.9%), a high risk group 30.2% (23.8%) and an extremely high risk group 78.6% (57.1%). Conclusions: LNM frequencies increased in proportion to the impact of the LNM score and the actual rate of lymph node metastasis for each score was quite consistent with the estimated rate of lymph node metastasis.Our LNM score for patients with endometrial carcinoma is useful. No significant financial relationships to disclose.

scholarly journals A Risk Signature of Nine Autophagy-Related Genes Associated With Immune Microenvironment and Clinical Prognosis in Wilms Tumor

2021 ◽  
Author(s):  
Shaopei Ye ◽  
Wenbin Tang ◽  
Ke Huang
Keyword(s):  
Regression Analysis ◽  
High Risk ◽  
Risk Score ◽  
Cox Regression ◽  
Wilms Tumor ◽  
Risk Group ◽  
High Risk Group ◽  
Differentially Expressed ◽  
Clinical Prognosis ◽  
Cox Regression Analysis

Abstract Background: Autophagy is a biological process to eliminate dysfunctional organelles, aggregates or even long-lived proteins. . Nevertheless, the potential function and prognostic values of autophagy in Wilms Tumor (WT) are complex and remain to be clarifed. Therefore, we proposed to systematically examine the roles of autophagy-associated genes (ARGs) in WT.Methods: Here, we obtained differentially expressed autophagy-related genes (ARGs) between healthy and Wilms tumor from Therapeutically Applicable Research To Generate Effective Treatments(TARGET) and The Cancer Genome Atlas (TCGA) database. The functionalities of the differentially expressed ARGs were analyzed using Gene Ontology. Then univariate COX regression analysis and multivariate COX regression analysis were performed to acquire nine autophagy genes related to WT patients’ survival. According to the risk score, the patients were divided into high-risk and low-risk groups. The Kaplan-Meier curve demonstrated that patients with a high-risk score tend to have a poor prognosis.Results: Eighteen DEARGs were identifed, and nine ARGs were fnally utilized to establish the FAGs based signature in the TCGA cohort. we found that patients in the high-risk group were associated with mutations in TP53. We further conducted CIBERSORT analysis, and found that the infiltration of Macrophage M1 was increased in the high-risk group. Finally, the expression levels of crucial ARGs were verifed by the experiment, which were consistent with our bioinformatics analysis.Conclusions: we emphasized the clinical significance of autophagy in WT, established a prediction system based on autophagy, and identified a promising therapeutic target of autophagy for WT.

scholarly journals Serum exosomal miR-4787-3p as potential lymph node metastasis and prognostic marker in esophageal squamous cell carcinoma.

2021 ◽  
Author(s):  
Shuang Liu ◽  
Zheng Lin ◽  
Jianwen Wang ◽  
Zerong Zheng ◽  
Wenqing Rao ◽  
...  
Keyword(s):  
Regression Analysis ◽  
Lymph Node ◽  
Lymph Node Metastasis ◽  
Cox Regression ◽  
High Serum ◽  
Migration And Invasion ◽  
Cox Regression Analysis ◽  
Mirna Array ◽  
Node Metastasis

Abstract Background: To explore the miR-4787-3p expression levels in the serum exosome and tissue and its role in lymph node metastasis and prognosis in ESCC. Methods: The miRNA array was conducted to detect the ESCC serum exosomal miRNAs expression. A receiver operating characteristic (ROC) curve was constructed to determine the predictive ESCC with lymph node metastasis efficacy of serum exosomal miR-4784-3p. The Cox regression analysis was preformed to explore prognostic factors for ESCC. Transwell assay and CCK-8 assays were utilized to evaluate cell migration, invasion, and proliferation, respectively. Results: High serum exosomal miR-4787-3p expression was demonstrated in lymph node metastasis group (P =0.011). The serum exosomal miR-4787-3p expression was significantly associated with histologic grade (P = 0.010), and TNM stage (P = 0.033). However, there was no significant relationship between tissue miR-4787-3p expression and clinical characteristics (P >0.05). ROC analyses revealed that the AUCs of serum exosomal miR-4787-3p for lymph node metastasis prediction was 0.787. The Cox regression analysis found that high expression serum exosomal miR-4787-3p were correlated with poor prognoses (for OS, HR=2.68, 95% CI: 1.02~7.04; for DFS, HR = 2.65, 95% CI: 1.05~6.68). Nevertheless, no association between tissue miR-4787-3p expression and ESCC prognosis. In addition, upregulated expression of miR-4787-3p could promote migration and invasion in vitro. Conclusions: Serum exosomal miR-4787-3p can be promising biomarkers for ESCC metastasis and prognosis

scholarly journals Identification of a Pyroptosis-Related Gene Signature for Predicting Overall Survival and Response to Immunotherapy in Hepatocellular Carcinoma

2021 ◽  
Vol 12 ◽  
Author(s):  
Susu Zheng ◽  
Xiaoying Xie ◽  
Xinkun Guo ◽  
Yanfang Wu ◽  
Guobin Chen ◽  
...  
Keyword(s):  
Hepatocellular Carcinoma ◽  
Overall Survival ◽  
High Risk ◽  
Regression Model ◽  
Risk Score ◽  
Cox Regression ◽  
Risk Group ◽  
Gene Signature ◽  
High Risk Group ◽  
Related Gene

Pyroptosis is a novel kind of cellular necrosis and shown to be involved in cancer progression. However, the diverse expression, prognosis and associations with immune status of pyroptosis-related genes in Hepatocellular carcinoma (HCC) have yet to be analyzed. Herein, the expression profiles and corresponding clinical characteristics of HCC samples were collected from the Cancer Genome Atlas (TCGA) and Gene Expression Omnibus (GEO) databases. Then a pyroptosis-related gene signature was built by applying the least absolute shrinkage and selection operator (LASSO) Cox regression model from the TCGA cohort, while the GEO datasets were applied for verification. Twenty-four pyroptosis-related genes were found to be differentially expressed between HCC and normal samples. A five pyroptosis-related gene signature (GSDME, CASP8, SCAF11, NOD2, CASP6) was constructed according to LASSO Cox regression model. Patients in the low-risk group had better survival rates than those in the high-risk group. The risk score was proved to be an independent prognostic factor for overall survival (OS). The risk score correlated with immune infiltrations and immunotherapy responses. GSEA indicated that endocytosis, ubiquitin mediated proteolysis and regulation of autophagy were enriched in the high-risk group, while drug metabolism cytochrome P450 and tryptophan metabolism were enriched in the low-risk group. In conclusion, our pyroptosis-related gene signature can be used for survival prediction and may also predict the response of immunotherapy.

scholarly journals Identification of a Novel Glycolysis-Related Signature to Predict the Prognosis of Patients with Breast Cancer

2021 ◽  
Author(s):  
Menglin He ◽  
Cheng Hu ◽  
Jian Deng ◽  
Hui Ji ◽  
Weiqian Tian
Keyword(s):  
Breast Cancer ◽  
Risk Score ◽  
Cox Regression ◽  
Risk Group ◽  
Gene Signature ◽  
High Risk Group ◽  
Low Risk ◽  
Cox Regression Analysis ◽  
Expression Levels ◽  
Incidence And Mortality

Abstract Background: Breast cancer (BC) is a kind of cancer with high incidence and mortality in female. Conventional clinical characteristics are far from accurate to predict individual outcomes. Therefore, we aimed to develop a novel signature to predict the survival of patients with BC. Methods: We analyzed the data of a training cohort from the TCGA database and a validation cohort from GEO database. After the applications of GSEA and Cox regression analyses, a glycolysis-related signature for predicting the survival of patients with BC was developed. The signature contains AK3, CACNA1H, IL13RA1, NUP43, PGK1, and SDC1. Then, we constructed a risk score formula to classify the patients into high and low-risk groups based on the expression levels of six-gene in patients. The receiver operating characteristic (ROC) curve and the Kaplan-Meier curve were used to assess the predicted capacity of the model. Next, a nomogram was developed to predict the outcomes of patients with risk score and clinical features in 1, 3, and 5 years. We further used Human Protein Atlas (HPA) database to validate the expressions of the six biomarkers in tumor and sample tissues.Results: We constructed a six-gene signature to predict the outcomes of patients with BC. The patients in high-risk group showed poor prognosis than that in low-risk group. The AUC values were 0.719 and 0.702, showing that the prediction performance of the signature is acceptable. Additionally, Cox regression analysis revealed that these biomarkers could independently predict the prognosis of BC patients without being affected by clinical factors. The expression levels of all six biomarkers in BC tissues were higher than that in normal tissues except AK3. Conclusion: We developed a six-gene signature to predict the prognosis of patients with BC. Our signature has been proved to have the ability to make an accurate and obvious prediction and might be used to expand the prediction methods in clinical.

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.

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

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

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

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