scholarly journals Prognostic and Predictive Value of m6A “Eraser” Related Gene Signature in Gastric Cancer

2021 ◽  
Vol 11 ◽  
Author(s):  
Xin Xu ◽  
En Zhou ◽  
Jun Zheng ◽  
Chihao Zhang ◽  
Yinghua Zou ◽  
...  
Keyword(s):  
Gastric Cancer ◽  
Prognostic Factors ◽  
Regression Model ◽  
Cox Regression ◽  
Risk Group ◽  
Critical Role ◽  
Predictive Tool ◽  
Cox Regression Model ◽  
Risk Genes ◽  
Related Risk

BackgroundN6-methyladenosine (m6A) RNA modification plays a critical role in gastric cancer (GC). However, the relationship between the m6A “eraser”, FTO, and ALKBH5, and the prognosis of GC still remains unclear. This study aimed to evaluate the effect of FTO and ALKBH5 on the prognosis of patients and their potential roles in GC.Materials and MethodsA total of 738 GC samples with clinical information obtained from two independent datasets were included and divided into training set and testing set. Differential expression analysis of the m6A “eraser” related genes was performed. The LASSO Cox regression model was constructed to analyze the m6A “eraser” related risk genes. The univariate and multivariate Cox regression model were employed to identify the independent prognostic factors. Kaplan-Meier method was used for survival analysis. A nomogram model was then carried out to predict the prognosis of GC patients. Additionally, GO and KEGG analyses were conducted to identify the potential role of the m6A “eraser” related genes in GC. The relative proportion of 22 different genotypes in immune infiltrating cells was calculated by CIBERSORT algorithm.ResultsIn total, nine m6A “eraser” related risk genes and risk scores were obtained and calculated. Patients in high-risk group demonstrated significantly worse prognosis than those in low-risk group. Age, stage, and risk score were considered as independent prognostic factors. The nomogram model constructed accurately predicted the 3-year and 5-year overall survival (OS) of patients. Furthermore, m6A “eraser” might play a functional role in GC. The expression of m6A “eraser” leads to changes in tumor immune microenvironment.ConclusionsFTO and ALKBH5 showed association with the prognosis of GC. The m6A “eraser” related genes, which is considered as a reliable prognostic and predictive tool, assists in predicting the OS in GC patients.

Determination of biologic and prognostic feature scores from whole slide histology images using deep learning.

2020 ◽  
Vol 38 (15_suppl) ◽  
pp. e17527-e17527 ◽  
Author(s):  
Okyaz Eminaga ◽  
Mahmoud Abbas ◽  
Axel Semjonow ◽  
James D Brooks ◽  
Daniel Rubin
Keyword(s):  
Deep Learning ◽  
Androgen Receptor ◽  
Regression Model ◽  
Cox Regression ◽  
Risk Group ◽  
Receptor Protein ◽  
Gleason Grade ◽  
Cox Regression Model ◽  
Survival Analyses ◽  
Histopathological Feature

e17527 Background: In cancer, histopathology is a reflection of the underlying molecular changes in the cancer cells and provides prognostic information on the risk of disease progression. Therefore, whole slide images may harbor histopathological features that have a biological association and are prognostic. Methods: This study has extracted histopathological feature scores generated from hematoxylin and eosin (HE) histology images based on deep learning models developed for the detection of pathological findings related to prostate cancer (PCa). Correlation analyses between the histopathological feature scores and the most relevant genomic alterations related to PCa were performed based on the original results and diagnostic histology images from TCGA PRAD study (n = 251). We extracted feature scores from tumor lesions after applying tumor segmentation and several data transformation using five models developed for detection of cribriform or ductal morphologies, Gleason patterns 3 and 4, and the presumed tumor precursor. For prognostic evaluation, we performed survival analyses of 371 patients from the TCGA PRAD dataset with biochemical recurrence (BCR) using a Cox regression model, Kaplan Meier (KM) curves. We applied the bootstrapping resampling for the uncertainty evaluation and C-statistics for the randomness measurement. Results: The feature scores were significantly correlated with the androgen receptor protein expression, an androgen-signaling score, mRNA expression, and androgen receptor splice variant 7. In addition, feature scores were associated with SPINK1 overexpression, the heterozygous loss of TP53, and SPOP mutations. Additionally, the mRNA and miRNA clusters identified by the TCGA research team for PCa. These features were independent of Gleason grade and were non-random. The survival analyses revealed that a model, including three of five feature scores, achieved a c-index of 0.706 (95% CI: 0.606-0.779). The KM curve showed that these risk groups based on the Cox regression model are significantly discriminative (Log-rank P-value < 0.0001). The low-risk group (n = 177) achieved a 2-year BCR-free survival rate (BFS) of 97.4% (95% CI: 94.9 - 100.0%) and a 5-year PFS of 88.3% (95% CI: 80.6 - 96.7%). In contrast, the high-risk group (n = 194) showed a 2-year PFS of 86.3% (95% CI: 81.1 - 91.8%) and a 5-year BFS of 66.9% (95% CI: 54.6 - 0.82.1%). Conclusions: Our findings uncover the potential of feature scores from histology images as digital biomarkers in precision medicine and as an expanding utility for digital pathology.

Development and validation of a robust immune-related risk signature for hepatocellular carcinoma

2020 ◽  
Author(s):  
Zaoqu Liu ◽  
Dechao Jiao ◽  
Xueliang Zhou ◽  
Yuan Yao ◽  
Zhaonan Li ◽  
...  
Keyword(s):  
Hepatocellular Carcinoma ◽  
Regression Analysis ◽  
Cox Regression ◽  
Risk Group ◽  
Low Risk ◽  
Functional Enrichment ◽  
Differentially Expressed ◽  
Risk Genes ◽  
Cox Regression Analysis ◽  
Related Risk

Abstract Background: A growing amount of evidence has suggested immune-related genes (IRGs) play a key role in the development of hepatocellular carcinoma (HCC). However, there have been no investigations proposing a reliable prognostic signature in terms of tumor immunology. This study aimed to develop a robust signature based on IRGs in HCC.Methods: A total of 597 HCC patients were enrolled. The TCGA database was utilized for discovery, and the ICGC database was utilized for validation. Multiple algorithms (including univariate Cox, LASSO, and multivariate Cox regression) were performed to identify key prognostic IRGs and establish an immune-related risk signature. Bioinformatics analysis and R soft tools were utilized to annotate underlying biological functions. Results: A total of 1416 differentially expressed mRNAs (DEMs) were screened in the TCGA cohort, of which 90 were differentially expressed IRGs (DEIRGs). Using univariate Cox regression analysis, we identified 33 prognostically relevant DEIRGs. Using LASSO regression and multivariate Cox regression analysis, we extracted 8 optimal DEIRGs (APLN, CDK4, CXCL2, ESR1, IL1RN, PSMD2, SEMA3F, and SPP1) to construct a risk signature with the ability to distinguish cases as having a high or low risk of unfavorable prognosis in the TCGA cohort, and the signature was verified in the ICGC cohort. The signature was prognostically significant in all stratified cohorts and was deemed an independent prognostic factor for HCC. We also built a nomogram with good performance by combining the signature with clinicopathological factors to increase the accuracy of predicting HCC prognosis. By investigating the relationship of the risk score and 8 risk genes from our signature with clinical traits, we found that the aberrant expression of the immune-related risk genes is correlated with the development of HCC. Moreover, the high-risk group was higher than the low-risk group in terms of tumor mutation burden (TMB), immune cell infiltration, and the expression of immune checkpoints (PD-1, PD-L1, and CTLA-4), and functional enrichment analysis indicated the signature enriched an intensive immune phenotype.Conclusions: This study developed a robust immune-related risk signature and built a predictive nomogram that reliably predict overall survival in HCC, which may be helpful for clinical management and personalized immunotherapy decisions.

scholarly journals Nomogram for predicting cancer-specific survival in undifferentiated pleomorphic sarcoma: A Surveillance, Epidemiology, and End Results -based study

2021 ◽  
Vol 28 ◽  
pp. 107327482110367
Author(s):  
Fengshuo Xu ◽  
Fanfan Zhao ◽  
Xiaojie Feng ◽  
Chengzhuo Li ◽  
Didi Han ◽  
...  
Keyword(s):  
Prognostic Factors ◽  
Regression Model ◽  
Cox Regression ◽  
External Validation ◽  
Cancer Specific Survival ◽  
Cox Regression Model ◽  
Training Cohort ◽  
Undifferentiated Pleomorphic Sarcoma ◽  
Pleomorphic Sarcoma ◽  
End Results

Introduction The purpose of this study was to construct and validate a nomogram for predicting cancer-specific survival (CSS) in undifferentiated pleomorphic sarcoma (UPS) patients at 3, 5, and 8 years after the diagnosis. Methods Data for UPS patients were extracted from the SEER (Surveillance, Epidemiology, and End Results) database. The patients were randomly divided into a training cohort (70%) and a validation cohort (30%). The backward stepwise Cox regression model was used to select independent prognostic factors. All of the factors were integrated into the nomogram to predict the CSS rates in UPS patients at 3, 5, and 8 years after the diagnosis. The nomogram’ s performance was then validated using multiple indicators, including the area under the time-dependent receiver operating characteristic curve (AUC), consistency index (C-index), calibration curve, decision-curve analysis (DCA), integrated discrimination improvement (IDI), and net reclassification improvement (NRI). Results This study included 2,009 UPS patients. Ten prognostic factors were identified after analysis of the Cox regression model in the training cohort, which were year of diagnosis, age, race, primary site, histological grade, T, N, M stage, surgery status, and insurance status. The nomogram was then constructed and validated internally and externally. The relatively high C-indexes and AUC values indicated that the nomogram has good discrimination ability. The calibration curves revealed that the nomogram was well calibrated. NRI and IDI values were both improved, indicating that our nomogram was superior to the AJCC (American Joint Committee on Cancer) system. DCA curves demonstrated that the nomogram was clinically useful. Conclusions The first nomogram for predicting the prognosis of UPS patients has been constructed and validated. Its usability and performance showed that the nomogram can be applied to clinical practice. However, further external validation is still needed.

Gastric cancer staging in the era of neoadjuvant therapy and its prognostic implications.

2019 ◽  
Vol 37 (4_suppl) ◽  
pp. 22-22
Author(s):  
Gina Kim ◽  
Patricia Friedmann ◽  
Peter Muscarella ◽  
John Christopher McAuliffe ◽  
Haejin In
Keyword(s):  
Gastric Cancer ◽  
Regression Model ◽  
Neoadjuvant Therapy ◽  
Cox Regression ◽  
Prognostic Information ◽  
Logrank Test ◽  
Cox Regression Model ◽  
Cancer Data ◽  
C Statistic ◽  
The Relationship

22 Background: Increasingly patients are undergoing neoadjuvant therapy for gastric cancer. The relationship between stage-based prognostic information available prior to treatment (cStage), after surgery (ypStage), and difference between cStage and ypStage (delta) remains unclear. We aim to describe the relationship between cStage and ypStage as relates to survival for gastric cancer patients. Methods: Data from the National Cancer Data Base (NCDB) from 2004-2015 was used for the analysis. Patients with gastric adenocarcinoma who received neoadjuvant therapy then underwent surgery were included. Kaplan Meier curves were used to model survival. Harrell’s C-statistics obtained from Cox Regression models were reported. Results: 9,959 patients met our inclusion criteria. Increases in cStage, ypStage and delta (ypStage-cStage) were associated with worse survival. Median overall survivals for cStages 1-4 were: 53.8, 39.5, 29.2, 20.9 months (logrank test, p<0.0001). Median survivals for ypStage 0-4 were: 95.4, 89.7, 36.9, 23.4, 16.0 months (logrank test, p<0.0001). Survival was further stratified by delta. A representative table comparing cStage 2 and ypStage 2 is shown below. A cox regression model with cStage as predictor of survival yielded a Harrell’s C-statistic of 0.555; when delta was added to the model, the C-statistic increased to 0.638. Separately, a Cox-regression model with ypStage as predictor yielded a C-statistic of 0.632; when delta was added to this model, the C-statistic increased negligibly to 0.638. Conclusions: Prognostic accuracy using cStage prior to treatment improved when tumor responsiveness was considered while this was not the case for ypStage. Pre-surgical prognostic information should be provided with a caveat that treatment response will influence survival. Post-surgery, the clinical stage is less relevant and ypStage can be used alone in providing prognostic information. [Table: see text]

scholarly journals RORα and REV-ERBα are Associated With Clinicopathological Parameters and are Independent Biomarkers of Prognosis in Gastric Cancer

2021 ◽  
Vol 20 ◽  
pp. 153303382110396
Author(s):  
Xiaoshan Wang ◽  
Ru Jia ◽  
Ke Chen ◽  
Jingjing Wang ◽  
Kai Jiang ◽  
...  
Keyword(s):  
Gastric Cancer ◽  
Regression Model ◽  
Cox Regression ◽  
Histological Grade ◽  
Model Analysis ◽  
Tnm Stage ◽  
Clinicopathological Parameters ◽  
Cox Regression Model ◽  
Expression Levels ◽  
Node Metastasis

Retinoid-related orphan receptor alpha (RORα) and nuclear receptor subfamily 1 group D member 1 (REV-ERBα) play critical roles in many human cancers. Whether RORα and REV-ERBα expression levels are associated with clinical characteristics are poorly understood, and they may be independent predictors of overall survival (OS) and progression-free survival (PFS) in gastric cancer (GC). This study aimed to investigate the correlation of RORα and REV-ERBα expression levels with clinicopathological parameters, OS, and PFS in GC. Immunohistochemistry and quantitative reverse transcription-polymerase chain reaction (qRT-PCR) were employed to assess the expression levels of RORα and REV-ERBα, which were downregulated in GC tissues compared with normal gastric tissues ( P < .001; P < .001) and were associated with several clinicopathological parameters, including histological grade ( P = .032; P < .001), preoperative carcinoembryonic antigen (CEA) levels ( P = .004; P < .001), and tumor-node-metastasis (TNM) stage ( P = .015; P < .001). Additionally, low RORα and REV-ERBα expression levels were associated with poor OS and PFS in GC patients, respectively ( P < .001; P = .001). Furthermore, univariate Cox regression model analysis showed that histological grade ( P < .001; P < .001), preoperative CEA levels ( P < .001; P = .001), TNM stage ( P < .001; P < .001), lymph node metastasis ( P = .002; P = .002), RORα expression levels ( P = .001; P < .001), and REV-ERBα expression levels ( P < .001; P = .001) were associated with OS and PFS in GC. Multivariate Cox regression model analysis indicated that RORα expression levels and REV-ERBα expression levels are independent factors of OS and PFS in GC. Besides, RORα and REV-ERBα expression may be positively correlated (χ2 = 6.835; P = .009), and GC patients with both high RORα and REV-ERBα expression levels had the best prognosis. In conclusion, RORα and REV-ERBα may coparticipate in tumor activities and show potential to estimate the prognosis of GC.

scholarly journals Prognostic Factors in Non-Occlusive Mesenteric Ischemia: A Pragmatic Pre-Operative Score for the Prediction of 28-day Mortality

2021 ◽  
Author(s):  
PAUL CALAME ◽  
Hadrien Winiszewski ◽  
Alexandre Doussot ◽  
Zaher Lakkis ◽  
Pierre Verdot ◽  
...  
Keyword(s):  
Prognostic Factors ◽  
Regression Model ◽  
Mesenteric Ischemia ◽  
Cox Regression ◽  
Cox Regression Model ◽  
Factors Associated ◽  
Icu Admission ◽  
Enhanced Ct ◽  
Occlusive Mesenteric Ischemia ◽  
Preoperative Factors

Abstract Background The prognosis of critical ill patients with non-occlusive mesenteric ischemia (NOMI) is poor and not fully understood. Preoperative prognostic factors are needed. The aim of this study was to determine preoperative factors associated with 28-day mortality in a cohort of ICU patients requiring laparotomy for NOMI. The secondary objective was to determine general prognostic factors associated with NOMI. Methods This retrospective observational study was performed in a University Hospital among critically ill patients 18 years old or older who underwent a laparotomy for NOMI from January 1, 2009 to December 31, 2019, and who had an available contrast enhanced CT with at least one portal venous phase. Variables were collected at the time of the CT. All variables associated with 28-day mortality were entered into a multivariate cox regression model and were used to compute a NOMI mortality score. Results During the study period, 154 patients underwent laparotomy for NOMI after having benefited from an abdominal enhanced CT. The 28-day mortality rate was 56%. Variable at the time of ICU admission and at the time of the CT were collected. Surgical and histopathologic findings were recorded. Multivariable analyses on 28-day mortality including variables at the time of the CT identified three independent variables (i.e. lactates > 7mmol/l, prothrombin rate < 60% and kidney infarction), included in a simple mortality score. For each variable associated with 28 days mortality, 1 point was attributed. Among the study population, the probability of 28-day mortality was 26% (11/42), 54% (26/48), 77% (23/30) and 100% (21/21) for a survival score of 0, 1, 2 and 3, respectively. A second explorative multivariate cox regression model including the variables at the time of ICU admission showed that jejunal transmural necrosis was the only operative finding associated with death (HR = 2.26 CI95%[1.14–4.71]). Conclusion We identified three preoperative factors independently associated with short-

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