Early atypical signs and insula hypometabolism predict survival in multiple system atrophy

ObjectiveWe aim to search for predictors of survival among clinical and brain 18F-FDG positron emission tomography (PET) metabolic features in our cohort of patients with multiple system atrophy (MSA).MethodsWe included patients with a ‘probable’ MSA diagnosis for whom a clinical evaluation and a brain PET were performed early in the course of the disease (median 3 years, IQR 2–5). A retrospective analysis was conducted using standardised data collection. Brain PET metabolism was characterised using the Automated Anatomical Labelling Atlas. A Cox model was applied to look for factors influencing survival. Kaplan-Meier method estimated the survival rate. We proposed to develop a predictive ‘risk score’, categorised into low-risk and high-risk groups, using significant variables entered in multivariate Cox regression analysis.ResultsEighty-five patients were included. The overall median survival was 8 years (CI 6.64 to 9.36). Poor prognostic factors were orthostatic hypotension (HR=6.04 (CI 1.58 to 23.12), p=0.009), stridor (HR=3.41 (CI 1.31 to 8.87), p=0.012) and glucose PET hypometabolism in the left insula (HR=0.78 (CI 0.66 to 0.92), p=0.004). Good prognostic factors were time to diagnosis (HR=0.68 (CI 0.54 to 0.86), p=0.001) and use of selective serotonin reuptake inhibitor (SSRI) (HR=0.17 (CI 0.06 to 0.46), p<0.001). The risk score revealed a 5-year gap separating the median survival of the two groups obtained (5 years vs 10 years; HR=5.82 (CI 2.94 to 11.49), p<0.001).ConclusionThe clinical prognosis factors we have described support published studies. Here, we also suggest that brain PET is of interest for prognosis assessment and in particular in the search for left insula hypometabolism. Moreover, SSRIs are a potential drug candidate to slow the progression of the disease.

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

10.21203/rs.3.rs-892534/v1 ◽

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.

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Comprehensive Characterization of Alternative mRNA Splicing Events in Glioblastoma: Implications for Prognosis, Molecular Subtypes, and Immune Microenvironment Remodeling

Frontiers in Oncology ◽

10.3389/fonc.2020.555632 ◽

2021 ◽

Vol 10 ◽

Author(s):

Liang Zhao ◽

Jiayue Zhang ◽

Zhiyuan Liu ◽

Yu Wang ◽

Shurui Xuan ◽

...

Keyword(s):

Risk Score ◽

Immune Cell ◽

Cox Regression ◽

Malignant Tumors ◽

Cox Model ◽

Molecular Classification ◽

The Cancer Genome Atlas ◽

Consensus Clustering ◽

Cox Regression Analysis ◽

Alternative Mrna Splicing

Alternative splicing (AS) of pre-mRNA has been widely reported to be associated with the progression of malignant tumors. However, a systematic investigation into the prognostic value of AS events in glioblastoma (GBM) is urgently required. The gene expression profile and matched AS events data of GBM patients were obtained from The Cancer Genome Atlas Project (TCGA) and TCGA SpliceSeq database, respectively. 775 AS events were identified as prognostic factors using univariate Cox regression analysis. The least absolute shrinkage and selection operator (LASSO) cox model was performed to narrow down candidate AS events, and a risk score model based on several AS events were developed subsequently. The risk score-based signature was proved as an efficient predictor of overall survival and was closely related to the tumor purity and immunosuppression in GBM. Combined similarity network fusion and consensus clustering (SNF-CC) analysis revealed two distinct GBM subtypes based on the prognostic AS events, and the associations between this novel molecular classification and clinicopathological factors, immune cell infiltration, as well as immunogenic features were further explored. We also constructed a regulatory network to depict the potential mechanisms that how prognostic splicing factors (SFs) regulate splicing patterns in GBM. Finally, a nomogram incorporating AS events signature and other clinical-relevant covariates was built for clinical application. This comprehensive analysis highlights the potential implications for predicting prognosis and clinical management in GBM.

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Super-Enhancer Associated Five-Gene Risk Score Model Predicts Overall Survival in Multiple Myeloma Patients

Frontiers in Cell and Developmental Biology ◽

10.3389/fcell.2020.596777 ◽

2020 ◽

Vol 8 ◽

Author(s):

Tingting Qi ◽

Jian Qu ◽

Chao Tu ◽

Qiong Lu ◽

Guohua Li ◽

...

Keyword(s):

Multiple Myeloma ◽

Prognostic Factors ◽

Risk Score ◽

Cox Regression ◽

Risk Group ◽

High Risk Group ◽

Bone Lesions ◽

Cox Regression Analysis ◽

Super Enhancer ◽

Score Model

Multiple myeloma (MM) is a malignant plasma cell tumor with high heterogeneity, characterized by anemia, hypercalcemia, renal failure, and lytic bone lesions. Although various powerful prognostic factors and models have been exploited, the development of more accurate prognosis and treatment for MM patients is still facing many challenges. Given the essential roles of super-enhancer (SE) associated genes in the tumorigenesis of MM, we tried to initially screen and identify the significant prognostic factors from SE associated genes in MM by the least absolute shrinkage and selection operator (Lasso) penalized Cox regression, univariate and multivariate Cox regression analysis using GSE24080 and GSE9782 datasets. Risk score model of five genes including CSGALNACT1, FAM53B, TAPBPL, REPIN1, and DDX11, was further constructed and the Kaplan-Meier (K-M) curves showed that the low-risk group seems to have better clinical outcome of survival compared to the high-risk group. Time-dependent receiver operating characteristic (ROC) curves presented the favorable performance of the model. An interactive nomogram consisting of the five-gene risk group and eleven clinical traits was established and identified by calibration curves. Therefore, the risk score model of SE associated five genes developed here could be used to predict the prognosis of MM patients, which may assist the clinical treatment of MM patients in the future.

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Prognostic Value of Combined Lactate Dehydrogenase, C-Reactive Protein, Cancer Antigen 153 and Cancer Antigen 125 in Metastatic Breast Cancer

Cancer Control ◽

10.1177/10732748211053150 ◽

2022 ◽

Vol 29 ◽

pp. 107327482110531

Author(s):

Yu-yuan Ma ◽

Han Wang ◽

Wei-dong Zhao ◽

Yi-Fan Li ◽

Jing-jing Wang ◽

...

Keyword(s):

Breast Cancer ◽

Regression Analysis ◽

Prognostic Factors ◽

Risk Score ◽

Cox Regression ◽

Ca 125 ◽

C Reactive Protein ◽

Cancer Antigen ◽

Cox Regression Analysis ◽

Reactive Protein

Background Breast cancer (BC), especially metastatic BC, is one of the most lethal diseases in women. CA 125 and CA 15-3 are commonly used indicators for diagnosis and prognosis of BC. Some serological indicators, such as lactate dehydrogenase (LDH) and C-reactive protein (CRP), can also be used to assess the prognosis and progression in BC. Methods Univariate Cox regression analysis and LASSO regression analysis were performed to identify prognostic factors and build prognostic models. We distributed the patients into 2 groups based on the median risk score, analyzed prognosis by Kaplan–Meier curve, and screened independent prognostic factors by multivariate Cox regression analysis. Result We identified 4 indicators-LDH, CRP, CA 15-3, and CA 125—related to the prognosis in BC and established a prognostic model. The high LDH group showed worse overall survival (OS) than low LDH group ( P = .017; hazard ratio (HR), 1.528; 95% confidence interval (CI), 1.055-2.215). The high CRP group showed worse OS than low CRP group ( P = .004; HR, 1.666; 95% CI, 1.143-2.429). The high CA153 group showed worse OS than low CA 15-3 group (P=.011; HR, 1.563; 95% CI, 1.075-2.274). The high CA 125 group showed worse OS than low CA 125 group ( P = .021; HR, 1.499; 95% CI, 1.031-2.181). The area under the curve for risk score was .824, Ki-67 was .628, age was .511, and grade was .545. Risk score was found to be an independent prognostic factor using multivariate Cox regression analysis. Conclusion We successfully established an optimization model by combining 4 prognosis-related indicators to assess the prognosis in patients with metastatic BC.

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Establishment of a Novel Risk Score System of Immune Genes Associated With Prognosis in Esophageal Carcinoma

Frontiers in Oncology ◽

10.3389/fonc.2021.625271 ◽

2021 ◽

Vol 11 ◽

Author(s):

Zhenghua Fei ◽

Rongrong Xie ◽

Zhi Chen ◽

Junhui Xie ◽

Yuyang Gu ◽

...

Keyword(s):

Risk Score ◽

Cox Regression ◽

Differentially Expressed ◽

Survival Outcomes ◽

Immune Gene ◽

Clinical Prognosis ◽

Cox Regression Analysis ◽

Immune Genes ◽

Normal Tissues ◽

Immune Related Genes

BackgroundFew studies have addressed the role of immune-related genes in the survival and prognosis of different esophageal cancer (EC) sub-types. We established two new prognostic model indexes by bioinformatics analysis to select patients with esophageal squamous cell carcinoma (ESCC) and esophageal adenocarcinoma (EAC) who may benefit from immunotherapy.MethodsBased on TCGA and ImmPort data sets, we screened immune genes differentially expressed between tumor and normal tissues in ESCC and EAC and analyzed the relationship between these genes and patient survival outcomes. We established the risk score models of immune-related genes in ESCC and EAC by multivariate COX regression analysis.ResultsWe identified 12 and 11 immune-related differentially expressed genes associated with the clinical prognosis of ESCC and EAC respectively, based on which two prognostic risk score models of the two EC sub-types were constructed. It was found that the survival probability of patients with high scores was significantly lower than that of patients with low scores (p < 0.001). BMP1, EGFR, S100A12, HLA-B, TNFSF18, IL1B, MAPT and OXTR were significantly related to sex, TNM stage or survival outcomes of ESCC or EAC patients (p < 0.05). In addition, the risk score of ESCC was significantly correlated with the level of B cell infiltration in immune cells (p < 0.05).ConclusionsThe prognosis-related immune gene model indexes described herein prove to be useful prognostic biomarkers of the two EC sub-types in that they may provide a reference direction for looking for the beneficiaries of immunotherapy for EC patients.

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Identification of the Ferroptosis-Related Long Non-Coding RNAs Signature to Improve the Prognosis Prediction in patients with NSCLC

10.21203/rs.3.rs-598005/v1 ◽

2021 ◽

Author(s):

Meng Li ◽

Yanpeng Zhang ◽

Meng Fan ◽

Hui Ren ◽

Mingwei Chen ◽

...

Keyword(s):

Risk Score ◽

Cox Regression ◽

Cox Model ◽

Expression Profiles ◽

External Validation ◽

Risk Groups ◽

Differentially Expressed ◽

Roc Curve Analysis ◽

Cox Regression Analysis ◽

Kaplan Meier

Abstract Background: Non-small cell lung cancer (NSCLC) is the most prevalent type of lung carcinoma with an unfavorable prognosis. Ferroptosis, a novel iron-dependent programmed cell death, is involved in the development of multiple cancers. Of note, the prognostic value of ferroptosis-related lncRNAs in NSCLC remains uncertain. Methods: Gene expression profiles and clinical information of NSCLC were retrieved from the TCGA database. Ferroptosis-related genes (FRGs) were explored in the FerrDb database and ferroptosis-related lncRNAs (FRGs-lncRNAs) were identified by the correlation analysis and the LncTarD database. Next, The differentially expressed FRGs-lncRNAs were screened and FRGs-lncRNAs associated with the prognosis were explored by univariate Cox regression analysis and Kaplan-Meier survival analysis. Then, an FRGs-lncRNAs signature was constructed by the Lasso-penalized Cox model in the training cohort and verified by internal and external validation. Finally, the potential correlation between risk score, immune response, and chemotherapeutic sensitivity was further investigated.Results: 129 lncRNAs with a potential regulatory relationship with 59 differentially expressed FRGs were found in NSCLC and 10 FRGs-lncRNAs associated with the prognosis of NSCLC were identified (P<0.05). 9 prognostic-related FRGs-lncRNAs (AQP4-AS1, DANCR, LINC00460, LINC00892, LINC00996, MED4-AS1, SNHG7, UCA1, and WWC2-AS2) were used to construct the prognostic model and stratify patients with NSCLC into high- and low-risk groups. Kaplan-Meier analysis demonstrated a worse outcome in patients with high risk (P<0.05). Moreover, a good predictive capacity of this signature in predicting NSCLC prognosis was confirmed by the ROC curve analysis. Additionally, 45 immune checkpoint genes and 8 m6A-related genes were found differentially expressed in the two risk groups, and the sensitivity of 28 chemotherapeutics were identified to be correlated with the risk score. Conclusion: A novel FRGs-lncRNAs signature was successfully constructed, which may contribute to improving the management strategies of NSCLC.

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Clinical Characteristics and Prognosis of Renal Cell Carcinoma With Spinal Bone Metastases

Frontiers in Oncology ◽

10.3389/fonc.2021.659779 ◽

2021 ◽

Vol 11 ◽

Author(s):

Jianpo Zhai ◽

Ning Liu ◽

Hai Wang ◽

Guanglin Huang ◽

Libo Man

Keyword(s):

Renal Cell Carcinoma ◽

Regression Analysis ◽

Prognostic Factors ◽

Survival Time ◽

Median Survival ◽

Median Survival Time ◽

Clinical Characteristics ◽

Cox Regression ◽

Visceral Metastasis ◽

Cox Regression Analysis

BackgroundThe prognosis of renal cell carcinoma (RCC) with spinal bone metastasis (sBM) varies greatly. In this study, we aimed to define the clinical characteristics and prognostic factors of RCC with spinal bone metastasis (sBM) in our center.MethodsThe clinical and medical records of RCC patients with sBMs were collected. The gender, age, time of BM, the extent of BM, the number of BMs, the presence or absence of visceral metastasis, and the pathological type of BM were investigated. All patients were followed up regularly. Overall survival (OS) was calculated from the date of BMs diagnosis to death or last follow-up using Kaplan-Meier method and modelled with Cox regression analysis.ResultsForty-three RCC patients with sBM were collected. sBM was found synchronously in 30 patients (70%) and metachronously in 13 patients (30%). The median survival time was 30 months in 13 patients (30%) with solitary sBM and 19 months in 30 patients (70%) with multiple sBMs (P = 0.002). Visceral metastasis occurred in 12 patients (28%) with the median survival time of 17 months, while the other 31 patients (72%) had no visceral metastasis with the median survival time of 29 months (P<0.001). En-block resection was done in 10 patients with median survival time of 40.1 months. Non-en-block resection were done in 33 patients with median survival time of 19.7 months (P<0.001). Multivariate COX regression analysis showed that MSKCC score, number of BM, visceral metastasis, and en-block resection are the independent prognosis factors of RCC patients with sBM.ConclusionsMSKCC risk stratification, number of sBM, visceral metastasis and en-block resection are significant prognostic factors for OS in RCC patients with spinal BM. Therefore, for selected patients who has solitary spinal BM with no visceral metastasis, en-block resection of spinal BM can potentially prolong survival and is the treatment of choice.

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Clinical characteristics and prognosis of renal cell carcinoma with spinal bone metastases

10.21203/rs.2.18443/v1 ◽

2019 ◽

Author(s):

Jianpo Zhai ◽

Ning Liu ◽

Hai Wang ◽

Haidong Wang ◽

Guanglin Huang ◽

...

Keyword(s):

Renal Cell Carcinoma ◽

Regression Analysis ◽

Prognostic Factors ◽

Survival Time ◽

Median Survival ◽

Median Survival Time ◽

Clinical Characteristics ◽

Cox Regression ◽

Visceral Metastasis ◽

Cox Regression Analysis

Abstract Background: The prognosis of renal cell carcinoma (RCC) with spinal bone metastasis (sBM) varies greatly. To define the clinical characteristics and prognostic factors of RCC with spinal bone metastasis (sBM) in our center. Methods: The clinical and medical records of RCC patients with sBMs were collected. The gender, age, time of BM, the extent of BM, the number of BMs, the presence or absence of visceral metastasis and the pathological type of BM were investigated. All patients were followed up regularly. OS was calculated from the date of BMs diagnosis to death or last follow-up using Kaplan-Meier method and modelled with Cox regression analysis. Results: 22 RCC patients with sBM were collected. sBM was found synchronously in 15 patients (68.2%) and metachronously in 7 patients (31.8%) . The median survival time was 30 months in 7 patients (31.8%) with solitary sBM and 19 months in 15 patients (68.2%) with multiple sBMs. Visceral metastasis occurred in 6 patients (27.3%)with the median survival time of 17 months, while the other 16 patients (72.7%) had no visceral metastasis with the median survival time of 29 months ( P =0.006). Enblock resection was done in 7 patients with median survival time of 34 months. Non-Enblock resection were done in 15 patients with median survival time of 18 months( P =0.006). Multivariate COX regression analysis showed that visceral metastasis and Enblock resection are the independent prognostic factors of RCC with sBM. Conclusions: No visceral metastasis, En-block resection are good prognostic factors for RCC with sBM. Therefore En-block resection of sBM is recommended for RCC without visceral metastasis.

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A glycolysis-based three-gene signature predicts survival in patients with lung squamous cell carcinoma

BMC Cancer ◽

10.1186/s12885-021-08360-z ◽

2021 ◽

Vol 21 (1) ◽

Author(s):

Guichuan Huang ◽

Jing Zhang ◽

Ling Gong ◽

Yi Huang ◽

Daishun Liu

Keyword(s):

Squamous Cell Carcinoma ◽

Cell Carcinoma ◽

Risk Score ◽

Squamous Cell ◽

Cox Regression ◽

Lung Squamous Cell Carcinoma ◽

Gene Signature ◽

Gene Set Enrichment Analysis ◽

Related Gene ◽

Cox Regression Analysis

Abstract Background Lung cancer is one of the most lethal and most prevalent malignant tumors worldwide, and lung squamous cell carcinoma (LUSC) is one of the major histological subtypes. Although numerous biomarkers have been found to be associated with prognosis in LUSC, the prediction effect of a single gene biomarker is insufficient, especially for glycolysis-related genes. Therefore, we aimed to develop a novel glycolysis-related gene signature to predict survival in patients with LUSC. Methods The mRNA expression files and LUSC clinical information were obtained from The Cancer Genome Atlas (TCGA) dataset. Results Based on Gene Set Enrichment Analysis (GSEA), we found 5 glycolysis-related gene sets that were significantly enriched in LUSC tissues. Univariate and multivariate Cox proportional regression models were performed to choose prognostic-related gene signatures. Based on a Cox proportional regression model, a risk score for a three-gene signature (HKDC1, ALDH7A1, and MDH1) was established to divide patients into high-risk and low-risk subgroups. Multivariate Cox regression analysis indicated that the risk score for this three-gene signature can be used as an independent prognostic indicator in LUSC. Additionally, based on the cBioPortal database, the rate of genomic alterations in the HKDC1, ALDH7A1, and MDH1 genes were 1.9, 1.1, and 5% in LUSC patients, respectively. Conclusion A glycolysis-based three-gene signature could serve as a novel biomarker in predicting the prognosis of patients with LUSC and it also provides additional gene targets that can be used to cure LUSC patients.

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Integrated analysis of methylation-driven genes and pretreatment prognostic factors in patients with hepatocellular carcinoma

BMC Cancer ◽

10.1186/s12885-021-08314-5 ◽

2021 ◽

Vol 21 (1) ◽

Author(s):

Dongsheng He ◽

Shengyin Liao ◽

Lifang Cai ◽

Weiming Huang ◽

Xuehua Xie ◽

...

Keyword(s):

Hepatocellular Carcinoma ◽

Prognostic Factors ◽

Risk Score ◽

Prognostic Model ◽

Cox Regression ◽

The Cancer Genome Atlas ◽

Integrated Analysis ◽

Calibration Plot ◽

Clinical Parameters ◽

T Stage

Abstract Background The potential reversibility of aberrant DNA methylation indicates an opportunity for oncotherapy. This study aimed to integrate methylation-driven genes and pretreatment prognostic factors and then construct a new individual prognostic model in hepatocellular carcinoma (HCC) patients. Methods The gene methylation, gene expression dataset and clinical information of HCC patients were downloaded from The Cancer Genome Atlas (TCGA) database. Methylation-driven genes were screened with a Pearson’s correlation coefficient less than − 0.3 and a P value less than 0.05. Univariable and multivariable Cox regression analyses were performed to construct a risk score model and identify independent prognostic factors from the clinical parameters of HCC patients. The least absolute shrinkage and selection operator (LASSO) technique was used to construct a nomogram that might act to predict an individual’s OS, and then C-index, ROC curve and calibration plot were used to test the practicability. The correlation between clinical parameters and core methylation-driven genes of HCC patients was explored with Student’s t-test. Results In this study, 44 methylation-driven genes were discovered, and three prognostic signatures (LCAT, RPS6KA6, and C5orf58) were screened to construct a prognostic risk model of HCC patients. Five clinical factors, including T stage, risk score, cancer status, surgical method and new tumor events, were identified from 13 clinical parameters as pretreatment-independent prognostic factors. To avoid overfitting, LASSO analysis was used to construct a nomogram that could be used to calculate the OS in HCC patients. The C-index was superior to that from previous studies (0.75 vs 0.717, 0.676). Furthermore, LCAT was found to be correlated with T stage and new tumor events, and RPS6KA6 was found to be correlated with T stage. Conclusion We identified novel therapeutic targets and constructed an individual prognostic model that can be used to guide personalized treatment in HCC patients.

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