Identification and validation of immunotypes for clear cell RCC prognosis

Abstract Purpose To develop an immunotype-based prognostic model for predicting the overall survival (OS) of patients with clear cell renal carcinoma (ccRCC). We explored novel immunotypes of patients with ccRCC, particularly those associated with overall survival. A risk-metastasis model was constructed by integrating the immunotypes with immune genes and used to test the accuracy of the immunotype model. Patients and Methods Patient cohort data were obtained from The Cancer Genome Atlas (TCGA) database, Gene Expression Omnibus (GEO) database, Renji database, and Surveillance, Epidemiology, and End Results (SEER) database. We employed the R software to select 3 immune cells and construct an immunotype-based prediction model. Immune genes selected using random Forest Algorithm were validated by immunohistochemistry (IHC). The H&L risk-metastasis model was constructed to assess the accuracy of the immunotype model through Multivariate COX regression analysis. Result Patients with ccRCC were categorized into immunotype H subgroup and immunotype L subgroup based on the overall survival rates. The immunotypes were found to be the independent prognostic index for ccRCC prognosis. As such, we constructed a new immunotypes-based SSIGN model. Three immune genes associated with difference between immunotype H and L were identified. An H&L risk-metastasis model was constructed to evaluate the accuracy of the immunotype model. Compared to the W-Risk-metastasis model which did not incorporate immunotypes, the H&L risk-metastasis model was more precise in predicting the survival of ccRCC patients. Conclusion The established immunotype model can effectively predict the survival of ccRCC patients. Except for mast cells, T cells and macrophages are positively associated with the overall survival of patients. The three immune genes identified, herein, can predict the survival rate of ccRCC patients, and expression of these immune genes is strongly linked to poor survival. The new SSIGN model provides an accurate tool for predicting the survival of ccRCC patients. H&L risk-metastasis model can effectively predict the risk of tumor metastasis.

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A Novel Six-mRNA Signature Predicts Survival of Patients With Glioblastoma Multiforme

Frontiers in Genetics ◽

10.3389/fgene.2021.634116 ◽

2021 ◽

Vol 12 ◽

Author(s):

Zhentao Liu ◽

Hao Zhang ◽

Hongkang Hu ◽

Zheng Cai ◽

Chengyin Lu ◽

...

Keyword(s):

Glioblastoma Multiforme ◽

Cox Regression ◽

Methylation Status ◽

Survival Rates ◽

Gene Expression Omnibus ◽

The Cancer Genome Atlas ◽

Survival Prediction ◽

Lasso Regression ◽

Cox Regression Analysis ◽

Score Model

Glioblastoma multiforme (GBM) is a devastating brain tumor and displays divergent clinical outcomes due to its high degree of heterogeneity. Reliable prognostic biomarkers are urgently needed for improving risk stratification and survival prediction. In this study, we analyzed genome-wide mRNA profiles in GBM patients derived from The Cancer Genome Atlas (TCGA) and Gene Expression Omnibus (GEO) databases to identify mRNA-based signatures for GBM prognosis with survival analysis. Univariate Cox regression model was used to evaluate the relationship between the expression of mRNA and the prognosis of patients with GBM. We established a risk score model that consisted of six mRNA (AACS, STEAP1, STEAP2, G6PC3, FKBP9, and LOXL1) by the LASSO regression method. The six-mRNA signature could divide patients into a high-risk and a low-risk group with significantly different survival rates in training and test sets. Multivariate Cox regression analysis confirmed that it was an independent prognostic factor in GBM patients, and it has a superior predictive power as compared with age, IDH mutation status, MGMT, and G-CIMP methylation status. By combining this signature and clinical risk factors, a nomogram can be established to predict 1-, 2-, and 3-year OS in GBM patients with relatively high accuracy.

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Epigenetic signature predicts overall survival clear cell renal cell carcinoma

Cancer Cell International ◽

10.1186/s12935-020-01640-x ◽

2020 ◽

Vol 20 (1) ◽

Author(s):

Yejinpeng Wang ◽

Liang Chen ◽

Lingao Ju ◽

Kaiyu Qian ◽

Xinghuan Wang ◽

...

Keyword(s):

Dna Methylation ◽

Renal Cell Carcinoma ◽

Overall Survival ◽

Cell Carcinoma ◽

Renal Cell ◽

Cox Regression ◽

Clear Cell ◽

The Cancer Genome Atlas ◽

Cell Renal Cell Carcinoma ◽

Cox Regression Analysis

Abstract Background Recently, increasing study have found that DNA methylation plays an important role in tumor, including clear cell renal cell carcinoma (ccRCC). Methods We used the DNA methylation dataset of The Cancer Genome Atlas (TCGA) database to construct a 31-CpG-based signature which could accurately predict the overall survival of ccRCC. Meanwhile, we constructed a nomogram to predict the prognosis of patients with ccRCC. Result Through LASSO Cox regression analysis, we obtained the 31-CpG-based epigenetic signature which were significantly related to the prognosis of ccRCC. According to the epigenetic signature, patients were divided into two groups with high and low risk, and the predictive value of the epigenetic signature was verified by other two sets. In the training set, hazard ratio (HR) = 13.0, 95% confidence interval (CI) 8.0–21.2, P < 0.0001; testing set: HR = 4.1, CI 2.2–7.7, P < 0.0001; entire set: HR = 7.2, CI 4.9–10.6, P < 0.0001, Moreover, combined with clinical indicators, the prediction of 5-year survival of ccRCC reached an AUC of 0.871. Conclusions Our study constructed a 31-CpG-based epigenetic signature that could accurately predicted overall survival of ccRCC and staging progression of ccRCC. At the same time, we constructed a nomogram, which may facilitate the prediction of prognosis for patients with ccRCC.

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Identification of the angiogenesis related genes for predicting prognosis of patients with gastric cancer

BMC Gastroenterology ◽

10.1186/s12876-021-01734-4 ◽

2021 ◽

Vol 21 (1) ◽

Author(s):

Sheng Zheng ◽

Zizhen Zhang ◽

Ning Ding ◽

Jiawei Sun ◽

Yifeng Lin ◽

...

Keyword(s):

Gastric Cancer ◽

Overall Survival ◽

High Efficiency ◽

Cox Regression ◽

Risk Group ◽

The Cancer Genome Atlas ◽

Differentially Expressed ◽

Lasso Regression ◽

Sequencing Data ◽

Cox Regression Analysis

Abstract Introduction Angiogenesis is a key factor in promoting tumor growth, invasion and metastasis. In this study we aimed to investigate the prognostic value of angiogenesis-related genes (ARGs) in gastric cancer (GC). Methods mRNA sequencing data with clinical information of GC were downloaded from The Cancer Genome Atlas (TCGA) and the Gene Expression Omnibus (GEO) databases. The differentially expressed ARGs between normal and tumor tissues were analyzed by limma package, and then prognosis‑associated genes were screened using Cox regression analysis. Nine angiogenesis genes were identified as crucially related to the overall survival (OS) of patients through least absolute shrinkage and selection operator (LASSO) regression. The prognostic model and corresponding nomograms were establish based on 9 ARGs and verified in in both TCGA and GEO GC cohorts respectively. Results Eighty-five differentially expressed ARGs and their enriched pathways were confirmed. Significant enrichment analysis revealed that ARGs-related signaling pathway genes were highly related to tumor angiogenesis development. Kaplan–Meier analysis revealed that patients in the high-risk group had worse OS rates compared with the low-risk group in training cohort and validation cohort. In addition, RS had a good prognostic effect on GC patients with different clinical features, especially those with advanced GC. Besides, the calibration curves verified fine concordance between the nomogram prediction model and actual observation. Conclusions We developed a nine gene signature related to the angiogenesis that can predict overall survival for GC. It’s assumed to be a valuable prognosis model with high efficiency, providing new perspectives in targeted therapy.

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Development and Validation of a Unique Gignature of Cancer Stemness-related Genes for Predicting the Overall survival of Colorectal Cancer Patients

10.21203/rs.3.rs-125323/v1 ◽

2020 ◽

Author(s):

Ran Wei ◽

Jichuan Quan ◽

Shuofeng Li ◽

Zhao Lu ◽

Xu Guan ◽

...

Keyword(s):

Colorectal Cancer ◽

Overall Survival ◽

Cox Regression ◽

The Cancer Genome Atlas ◽

Cancer Stemness ◽

Poor Overall Survival ◽

Tissue Samples ◽

Cox Regression Analysis ◽

Rna Methylation ◽

M6a Rna Methylation

Abstract Background: Cancer stem cells (CSCs), which are characterized by self-renewal and plasticity, are highly correlated with tumor metastasis and drug resistance. To fully understand the role of CSCs in colorectal cancer (CRC), we evaluated the stemness traits and prognostic value of stemness-related genes in CRC.Methods: In this study, the data from 616 CRC patients from The Cancer Genome Atlas (TCGA) were assessed and subtyped based on the mRNA expression-based stemness index (mRNAsi). The correlations of cancer stemness with the immune microenvironment, tumor mutational burden (TMB) and N6-methyladenosine (m6A) RNA methylation regulators were analyzed. Weighted gene co-expression network analysis (WGCNA) was performed to identify the crucial stemness-related genes and modules. Furthermore, a prognostic expression signature was constructed using Lasso-penalized Cox regression analysis. The signature was validated via multiplex immunofluorescence staining of tissue samples in an independent cohort of 48 CRC patients.Results: This study suggests that high mRNAsi scores are associated with poor overall survival in stage Ⅳ CRC patients. Moreover, the levels of TMB and m6A RNA methylation regulators were positively correlated with mRNAsi scores, and low mRNAsi scores were characterized by increased immune activity in CRC. The analysis identified 2 key modules and 34 key genes as prognosis-related candidate biomarkers. Finally, a 3-gene prognostic signature (PARPBP, KNSTRN and KIF2C) was explored together with specific clinical features to construct a nomogram, which was successfully validated in an external cohort. Conclusions: There is a unique correlation between CSCs and the prognosis of CRC patients, and the novel biomarkers related to cell stemness could accurately predict the clinical outcomes of these patients.

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Surgical Resection Significantly Promotes the Overall Survival of Patients with Hepatocellular Carcinoma: A Propensity Score Matching Analysis

10.21203/rs.3.rs-148506/v1 ◽

2021 ◽

Author(s):

Pei-Min Hsieh ◽

Hung-Yu Lin ◽

Chao-Ming Hung ◽

Gin-Ho Lo ◽

I-Cheng Lu ◽

...

Keyword(s):

Risk Factors ◽

Hepatocellular Carcinoma ◽

Overall Survival ◽

Propensity Score ◽

Propensity Score Matching ◽

Surgical Resection ◽

Cox Regression ◽

Survival Rates ◽

Hbv Infection ◽

Cox Regression Analysis

Abstract Background: The benefits of surgical resection (SR) for various Barcelona Clinic Liver Cancer (BCLC) stages of hepatocellular carcinoma (HCC) remain unclear. We investigated the risk factors of overall survival (OS) and survival benefits of SR over nonsurgical treatments in patients with HCC of various BCLC stages.Methods: Overall, 2316 HCC patients were included, and their clinicopathological data and OS were recorded. OS was analyzed by the Kaplan-Meier method and Cox regression analysis. Propensity score matching (PSM) analysis was performed.Results: In total, 66 (2.8%), 865 (37.4%), 575 (24.8%) and 870 (35.0%) patients had BCLC stage 0, A, B, and C disease, respectively. Furthermore, 1302 (56.2%) of all patients, and 37 (56.9%), 472 (54.6%), 313 (54.4%) and 480 (59.3%) of patients with BCLC stage 0, A, B, and C disease, respectively, died. The median follow-up duration time was 20 (range 0-96) months for the total cohort and was subdivided into 52 (8-96), 32 (1-96), 19 (0-84), and 12 (0-79) months for BCLC stages 0, A, B, and C cohorts, respectively. The risk factors for OS were 1) SR and cirrhosis; 2) SR, cirrhosis, and Child-Pugh (C-P) class; 3) SR, hepatitis B virus (HBV) infection, and C-P class; and 4) SR, HBV infection, and C-P class for the BCLC stage 0, A, B, and C cohorts, respectively. Compared to non-SR treatment, SR resulted in significantly higher survival rates in all cohorts. The 5-year OS rates for SR vs non-SR were 44.0% vs 28.7%, 72.2% vs 42.6%, 42.6% vs 36.2, 44.6% vs 23.5%, and 41.4% vs 15.3% (all p-values<0.05) in the total and BCLC stage 0, A, B, and C cohorts, respectively. After PSM, SR resulted in significantly higher survival rates compared to non-SR treatment in various BCLC stages.Conclusion: SR conferred significant survival benefits to patients with HCC of various BCLC stages and should be considered a recommended treatment for select HCC patients, especially patients with BCLC stage B and C disease.

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A novel 10 glycolysis-related genes signature could predict overall survival for clear cell renal cell carcinoma

BMC Cancer ◽

10.1186/s12885-021-08111-0 ◽

2021 ◽

Vol 21 (1) ◽

Author(s):

Qianwei Xing ◽

Tengyue Zeng ◽

Shouyong Liu ◽

Hong Cheng ◽

Limin Ma ◽

...

Keyword(s):

Renal Cell Carcinoma ◽

Overall Survival ◽

Cell Carcinoma ◽

Renal Cell ◽

Cox Regression ◽

Clear Cell ◽

Low Risk ◽

The Cancer Genome Atlas ◽

Regression Analyses ◽

Cell Renal Cell Carcinoma

Abstract Background The role of glycolysis in tumorigenesis has received increasing attention and multiple glycolysis-related genes (GRGs) have been proven to be associated with tumor metastasis. Hence, we aimed to construct a prognostic signature based on GRGs for clear cell renal cell carcinoma (ccRCC) and to explore its relationships with immune infiltration. Methods Clinical information and RNA-sequencing data of ccRCC were obtained from The Cancer Genome Atlas (TCGA) and ArrayExpress datasets. Key GRGs were finally selected through univariate COX, LASSO and multivariate COX regression analyses. External and internal verifications were further carried out to verify our established signature. Results Finally, 10 GRGs including ANKZF1, CD44, CHST6, HS6ST2, IDUA, KIF20A, NDST3, PLOD2, VCAN, FBP1 were selected out and utilized to establish a novel signature. Compared with the low-risk group, ccRCC patients in high-risk groups showed a lower overall survival (OS) rate (P = 5.548Ee-13) and its AUCs based on our established signature were all above 0.70. Univariate/multivariate Cox regression analyses further proved that this signature could serve as an independent prognostic factor (all P < 0.05). Moreover, prognostic nomograms were also created to find out the associations between the established signature, clinical factors and OS for ccRCC in both the TCGA and ArrayExpress cohorts. All results remained consistent after external and internal verification. Besides, nine out of 21 tumor-infiltrating immune cells (TIICs) were highly related to high- and low- risk ccRCC patients stratified by our established signature. Conclusions A novel signature based on 10 prognostic GRGs was successfully established and verified externally and internally for predicting OS of ccRCC, helping clinicians better and more intuitively predict patients’ survival.

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Identification and validation of a hub gene prognostic index for hepatocellular carcinoma

Future Oncology ◽

10.2217/fon-2020-1112 ◽

2021 ◽

Author(s):

Q Shi ◽

Z Meng ◽

XX Tian ◽

YF Wang ◽

WH Wang

Keyword(s):

Hepatocellular Carcinoma ◽

Cox Regression ◽

Prognostic Index ◽

Interaction Network ◽

Gene Signature ◽

Gene Expression Omnibus ◽

Cox Regression Analysis ◽

Protein Protein Interaction ◽

Normal Tissues ◽

Significant Independent Prognostic Factor

Aims: We aim to provide new insights into the mechanisms of hepatocellular carcinoma (HCC) and identify key genes as biomarkers for the prognosis of HCC. Materials & methods: Differentially expressed genes between HCC tissues and normal tissues were identified via the Gene Expression Omnibus tool. The top ten hub genes screened by the degree of the protein nodes in the protein–protein interaction network also showed significant associations with overall survival in HCC patients. Results: A prognostic model containing a five-gene signature was constructed to predict the prognosis of HCC via multivariate Cox regression analysis. Conclusion: This study identified a novel five-gene signature ( CDK1, CCNB1, CCNB2, BUB1 and KIF11) as a significant independent prognostic factor.

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Clinical factors affecting prognosis of limb osteosarcoma in China: a multicenter retrospective analysis

Journal of International Medical Research ◽

10.1177/0300060520920053 ◽

2020 ◽

Vol 48 (8) ◽

pp. 030006052093085

Author(s):

Jia Han ◽

Yiyang Yu ◽

Sujia Wu ◽

Zhen Wang ◽

Weibin Zhang ◽

...

Keyword(s):

Overall Survival ◽

Survival Rate ◽

Cox Regression ◽

Survival Rates ◽

Treatment Method ◽

Overall Survival Rate ◽

Clinical Factors ◽

Standard Chemotherapy ◽

Factors Affecting ◽

Cox Regression Analysis

Objective This study was performed to explore the relationship between various clinical factors and the prognosis of limb osteosarcoma. Methods We retrospectively analyzed the clinical data of 336 patients with limb osteosarcoma treated from June 2000 to August 2016 at 7 Chinese cancer centers. Data on the patients’ clinical condition, treatment method, complications, recurrences, metastasis, and prognosis were collected and analyzed. Kaplan–Meier analysis and Cox regression models were used to analyze the data. Results The patients comprised 204 males and 132 females ranging in age from 6 to 74 years (average, 21.1 years). The overall 3- and 5-year survival rates were 65.0% and 55.0%, respectively. The 5-year overall survival rate was 64.0% with standard chemotherapy and 45.6% with non-standard chemotherapy. Cox regression analysis demonstrated that standard chemotherapy, surgery, recurrence, and metastasis were independent factors associated with the prognosis of limb osteosarcoma. Conclusion The survival of patients with limb osteosarcoma can be significantly improved by combining standard chemotherapy and surgery. The overall survival rate can also be improved by adding methotrexate to doxorubicin–cisplatin–ifosfamide triple chemotherapy.

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Pediatric Patients with Adrenal Neuroblastoma: A SEER Analysis, 2004–2013

The American Surgeon ◽

10.1177/000313482008600232 ◽

2020 ◽

Vol 86 (2) ◽

pp. 127-133

Author(s):

Shengxiang Chen ◽

Wenfeng Tang ◽

Randong Yang ◽

Xiaoxiao Hu ◽

Zhongrong Li

Keyword(s):

Overall Survival ◽

Pediatric Patients ◽

Cox Regression ◽

Demographic Data ◽

Survival Rates ◽

Cox Regression Analysis ◽

Kaplan Meier ◽

Survival Difference ◽

Tumor Management ◽

End Results

Adrenal neuroblastoma (NB) is a relatively common malignancy in children. The Surveillance, Epidemiology, and End Results database was used to present demographic data and a survival analysis with the aim of making tumor management better. The Surveillance, Epidemiology, and End Results database was used to search pediatric patients (age £16 years) with NB from 2004 to 2013. The Kaplan-Meier method was used to calculate the overall survival. And, we used Cox regression analysis to determine hazard ratios for prognostic variables. Independent prognostic factors were selected into the nomogram to predict individual's three-, five-, and seven-year overall survival. The study included a total of 1870 pediatric patients with NB in our cohort. Overall, three-, five-, and seven-year survival rates for adrenal NB were 0.777, 0.701, and 0.665, respectively, whereas the rates for nonadrenal NB were 0.891, 0.859, and 0.832, respectively. The multivariate analysis identified age >1 year, no complete resection (CR)/CR, radiation, and regional/distant metastasis as independent predictors of mortality for adrenal NB. Concordance index of the nomogram was 0.665 (95% confidence interval, 0.627–0.703). Pediatric patients with adrenal NB have significantly worse survival than those with nonadrenal NB. Adrenal NB with age <1 year, treated with surgery, no radiation, and localized tumor leads to a better survival. There was no survival difference for patients to receive CR and no CR.

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Analysis of Immune-Related Signatures Related to CD4+ T Cell Infiltration With Gene Co-Expression Network in Pancreatic Adenocarcinoma

Frontiers in Oncology ◽

10.3389/fonc.2021.674897 ◽

2021 ◽

Vol 11 ◽

Author(s):

Zhen Tan ◽

Yubin Lei ◽

Bo Zhang ◽

Si Shi ◽

Jiang Liu ◽

...

Keyword(s):

Overall Survival ◽

Molecular Mechanisms ◽

Cox Regression ◽

Risk Groups ◽

Gene Signature ◽

Gene Expression Omnibus ◽

Ductal Adenocarcinoma ◽

Rank Test ◽

Cancer Center ◽

Cox Regression Analysis

BackgroundPancreatic ductal adenocarcinoma (PDAC) is one of the most invasive solid malignancies. Immunotherapy and targeted therapy confirmed an existing certain curative effect in treating PDAC. The aim of this study was to develop an immune-related molecular marker to enhance the ability to predict Stages III and IV PDAC patients.MethodIn this study, weighted gene co-expression network (WGCNA) analysis and a deconvolution algorithm (CIBERSORT) that evaluated the cellular constituent of immune cells were used to evaluate PDAC expression data from the GEO (Gene Expression Omnibus) datasets, and identify modules related to CD4+ T cells. LASSO Cox regression analysis and Kaplan–Meier curve were applied to select and build prognostic multi-gene signature in TCGA Stages III and IV PDAC patients (N = 126). This was followed by independent Stages III and IV validation of the gene signature in the International Cancer Genome Consortium (ICGC, N = 62) and the Fudan University Shanghai Cancer Center (FUSCC, N = 42) cohort. Inherited germline mutations and tumor immunity exploration were applied to elucidate the molecular mechanisms in PDAC. Univariate and Multivariate Cox regression analyses were applied to verify the independent prognostic factors. Finally, a prognostic nomogram was created according to the TCGA-PDAC dataset.ResultsA four-gene signature comprising NAPSB, ZNF831, CXCL9 and PYHIN1 was established to predict overall survival of PDAC. This signature also robustly predicted survival in two independent validation cohorts. The four-gene signature could divide patients into high and low-risk groups with disparity overall survival verified by a Log-rank test. Expression of four genes positively correlated with immunosuppression activity (PD-L1 and PD1). Immune-related genes nomogram and corresponding calibration curves showed significant performance for predicting 3-year survival in TCGA-PDAC dataset.ConclusionWe constructed a novel four-gene signature to predict the prognosis of Stages III and IV PDAC patients by applying WGCNA and CIBERSORT algorithm scoring to transcriptome data different from traditional methods of filtrating for differential genes in cancer and healthy tissues. The findings may provide reference to predict survival and was beneficial to individualized management for advanced PDAC patients.

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