scholarly journals Identification of the subtypes of gastric cancer based on DNA methylation and the prediction of prognosis

2020 ◽  
Vol 12 (1) ◽  
Author(s):  
Tengda Li ◽  
Xin Chen ◽  
Mingli Gu ◽  
Anmei Deng ◽  
Cheng Qian
Keyword(s):  
Gastric Cancer ◽  
High Risk ◽  
Transforming Growth Factor ◽  
Risk Model ◽  
Low Risk ◽  
High Survival ◽  
Ample Evidence ◽  
Cpg Sites ◽  
Tumor Stages ◽  
Risk Patients

Abstract Background Gastric cancer (GC) is a digestive system cancer with a high mortality rate globally. Previous experiences and studies have provided clinicians with ample evidence to diagnose and treat patients with reasonable therapeutic options. However, there remains a need for sensitive biomarkers that can provide clues for early diagnosis and prognosis assessment. Results We found 610 independent prognosis-related 5′-cytosine-phosphate-guanine-3′ (CpG) sites (P < 0.05) among 21,121 sites in the training samples. We divided the GC samples into seven clusters based on the selected 610 sites. Cluster 6 had relatively higher methylation levels and high survival rates than the other six clusters. A prognostic risk model was constructed using the significantly altered CpG sites in cluster 6 (P < 0.05). This model could distinguish high-risk GC patients from low-risk groups efficiently with the area under the receiver operating characteristic curve of 0.92. Risk assessment showed that the high-risk patients had poorer prognosis than the low-risk patients. The methylation levels of the selected sites in the established model decreased as the risk scores increased. This model had been validated in testing group and its effectiveness was confirmed. Corresponding genes of the independent prognosis-associated CpGs were identified, they were enriched in several pathways such as pathways in cancer and gastric cancer. Among all of the genes, the transcript level of transforming growth factor β2 (TGFβ2) was changed in different tumor stages, T categories, grades, and patients’ survival states, and up-regulated in patients with GC compared with the normal. It was included in the pathways as pathways in cancer, hepatocellular carcinoma or gastric cancer. The methylation site located on the promoter of TGFβ2 was cg11976166. Conclusions This is the first study to separate GC into different molecular subtypes based on the CpG sites using a large number of samples. We constructed an effective prognosis risk model that can identify high-risk GC patients. The key CpGs sites or their corresponding genes such as TGFβ2 identified in this research can provide new clues that will enable gastroenterologists to make diagnosis or personalized prognosis assessments and better understand this disease.

scholarly journals Accuracy of upper endoscopies with random biopsies to identify patients with gastric premalignant lesions who can safely be exempt from surveillance

2021 ◽  
Vol 24 (3) ◽  
pp. 680-690
Author(s):  
Michiel C. Mommersteeg ◽  
Stella A. V. Nieuwenburg ◽  
Wouter J. den Hollander ◽  
Lisanne Holster ◽  
Caroline M. den Hoed ◽  
...  
Keyword(s):  
Gastric Cancer ◽  
High Risk ◽  
Low Risk ◽  
Premalignant Lesions ◽  
High Risk Patients ◽  
European Guidelines ◽  
Progression Of Disease ◽  
Risk Patients ◽  
Progression Risk

Abstract Introduction Guidelines recommend endoscopy with biopsies to stratify patients with gastric premalignant lesions (GPL) to high and low progression risk. High-risk patients are recommended to undergo surveillance. We aimed to assess the accuracy of guideline recommendations to identify low-risk patients, who can safely be discharged from surveillance. Methods This study includes patients with GPL. Patients underwent at least two endoscopies with an interval of 1–6 years. Patients were defined ‘low risk’ if they fulfilled requirements for discharge, and ‘high risk’ if they fulfilled requirements for surveillance, according to European guidelines (MAPS-2012, updated MAPS-2019, BSG). Patients defined ‘low risk’ with progression of disease during follow-up (FU) were considered ‘misclassified’ as low risk. Results 334 patients (median age 60 years IQR11; 48.7% male) were included and followed for a median of 48 months. At baseline, 181/334 (54%) patients were defined low risk. Of these, 32.6% were ‘misclassified’, showing progression of disease during FU. If MAPS-2019 were followed, 169/334 (51%) patients were defined low risk, of which 32.5% were ‘misclassified’. If BSG were followed, 174/334 (51%) patients were defined low risk, of which 32.2% were ‘misclassified’. Seven patients developed gastric cancer (GC) or dysplasia, four patients were ‘misclassified’ based on MAPS-2012 and three on MAPS-2019 and BSG. By performing one additional endoscopy 72.9% (95% CI 62.4–83.3) of high-risk patients and all patients who developed GC or dysplasia were identified. Conclusion One-third of patients that would have been discharged from GC surveillance, appeared to be ‘misclassified’ as low risk. One additional endoscopy will reduce this risk by 70%.

scholarly journals A Novel Autophagy-Related lncRNA Gene Signature to Improve the Prognosis of Patients with Melanoma

2020 ◽  
Author(s):  
Yi Ding ◽  
Tian Li ◽  
Min Li ◽  
Tuersong Tayier ◽  
MeiLin Zhang ◽  
...  
Keyword(s):  
Overall Survival ◽  
High Risk ◽  
Risk Model ◽  
Risk Group ◽  
Clinical Information ◽  
Low Risk ◽  
Gene Set Enrichment Analysis ◽  
Positive Regulation ◽  
Risk Patients ◽  
Cox Analysis

Abstract Background: Autophagy and long non-coding RNAs (lncRNAs) have been the focus of research on the pathogenesis of melanoma. However, the autophagy network of lncRNAs in melanoma has not been reported. The purpose of this study was to investigate the lncRNA prognostic markers related to melanoma autophagy and predict the prognosis of patients with melanoma.Methods: We downloaded RNA-sequencing data and clinical information of melanoma from The Cancer Genome Atlas. The co-expression of autophagy-related genes (ARGs) and lncRNAs was analyzed. The risk model of autophagy-related lncRNAs was established by univariate and multivariate COX regression analyses, and the best prognostic index was evaluated combined with clinical data. Finally, gene set enrichment analysis was performed on patients in the high- and low-risk groups.Results: According to the results of the univariate COX analysis, only the overexpression of LINC00520 was associated with poor overall survival, unlike HLA-DQB1-AS1, USP30-AS1, AL645929, AL365361, LINC00324, and AC055822. The results of the multivariate COX analysis showed that the overall survival of patients in the high-risk group was shorter than that recorded in the low-risk group (p<0.001). Moreover, in the receiver operating characteristic curve of the risk model we constructed, the area under the curve (AUC) was 0.734, while the AUC of T and N was 0.707 and 0.658, respectively. The Gene Ontology was mainly enriched with the positive regulation of autophagy and the activation of the immune system. The results of the Kyoto Encyclopedia of Genes and Genomes enrichment were mostly related to autophagy, immunity, and melanin metabolism.Conclusion: The positive regulation of autophagy may slow the transition from low-risk patients to high-risk patients in melanoma. Furthermore, compared with clinical information, the autophagy-related lncRNAs risk model may better predict the prognosis of patients with melanoma and provide new treatment ideas.

scholarly journals A Tumor Progression Related 7-Gene Signature Indicates Prognosis and Tumor Immune Characteristics of Gastric Cancer

2021 ◽  
Vol 11 ◽  
Author(s):  
Fen Liu ◽  
Zongcheng Yang ◽  
Lixin Zheng ◽  
Wei Shao ◽  
Xiujie Cui ◽  
...  
Keyword(s):  
Gastric Cancer ◽  
High Risk ◽  
Cancer Progression ◽  
Risk Score ◽  
Risk Model ◽  
Cox Regression ◽  
Hub Genes ◽  
Cox Regression Analysis ◽  
High Risk Patients ◽  
Risk Patients

BackgroundGastric cancer is a common gastrointestinal malignancy. Since it is often diagnosed in the advanced stage, its mortality rate is high. Traditional therapies (such as continuous chemotherapy) are not satisfactory for advanced gastric cancer, but immunotherapy has shown great therapeutic potential. Gastric cancer has high molecular and phenotypic heterogeneity. New strategies for accurate prognostic evaluation and patient selection for immunotherapy are urgently needed.MethodsWeighted gene coexpression network analysis (WGCNA) was used to identify hub genes related to gastric cancer progression. Based on the hub genes, the samples were divided into two subtypes by consensus clustering analysis. After obtaining the differentially expressed genes between the subtypes, a gastric cancer risk model was constructed through univariate Cox regression, least absolute shrinkage and selection operator (LASSO) regression and multivariate Cox regression analysis. The differences in prognosis, clinical features, tumor microenvironment (TME) components and immune characteristics were compared between subtypes and risk groups, and the connectivity map (CMap) database was applied to identify potential treatments for high-risk patients.ResultsWGCNA and screening revealed nine hub genes closely related to gastric cancer progression. Unsupervised clustering according to hub gene expression grouped gastric cancer patients into two subtypes related to disease progression, and these patients showed significant differences in prognoses, TME immune and stromal scores, and suppressive immune checkpoint expression. Based on the different expression patterns between the subtypes, we constructed a gastric cancer risk model and divided patients into a high-risk group and a low-risk group based on the risk score. High-risk patients had a poorer prognosis, higher TME immune/stromal scores, higher inhibitory immune checkpoint expression, and more immune characteristics suitable for immunotherapy. Multivariate Cox regression analysis including the age, stage and risk score indicated that the risk score can be used as an independent prognostic factor for gastric cancer. On the basis of the risk score, we constructed a nomogram that relatively accurately predicts gastric cancer patient prognoses and screened potential drugs for high-risk patients.ConclusionsOur results suggest that the 7-gene signature related to tumor progression could predict the clinical prognosis and tumor immune characteristics of gastric cancer.

scholarly journals A SEER population analysis of stage IB resected gastric cancer: Who can benefit from adjuvant therapy?

2019 ◽  
Vol 5 (suppl) ◽  
pp. 122-122
Author(s):  
Yue Wang
Keyword(s):  
Gastric Cancer ◽  
High Risk ◽  
Adjuvant Therapy ◽  
Clinical Decision Making ◽  
Family Income ◽  
Population Analysis ◽  
Low Risk ◽  
High Risk Patients ◽  
Stage Ib ◽  
Risk Patients

122 Background: The benefit of adjuvant therapy (AT) remains controversial in stage IB gastric cancer (GC). This study aimed to offer a reference for the rational indications of AT. Methods: We retrospectively included 1935 stage IB GC patients who experienced curative surgery from the SEER database between 2004 and 2015. These patients were allocated into two groups: Group AT and Group surgery alone (Group SA). Risk factors associated with AT were examined using univariate/multivariate analyses. A nomogram to project overall survival (OS) of AT was established and internally validated. Results: Five variables, which were significantly related with OS of AT, were incorporated in the nomogram. These variables were sex, age, examined lymph nodes, tumor site, and family income. The C-index of the model was 0.636 and the calibration curve showed that the anticipated values were in accordance with the actual values. The decision curve demonstrated that the optimal clinical impact was achieved when the threshold possibility was 0-47%. Then the entire cohort was separated into low-risk (≤107 points) as well as high-risk ( > 107 points) groups based on the projected 5-year OS. Group SA revealed a significantly poorer OS than Group AT for high-risk patients (P < 0.001); on the other hand, there was a comparable OS for low-risk patients (P = 0.067). Conclusions: We have developed an effective, intuitional and applied prognostic tool based on nomogram to clinical decision-making. For stage IB GC after surgical resection, AT was only recommended for high-risk patients. However, AT may be dispensable for low-risk patients.

Construction of a Prognostic Model Related to Ferroptosis and Iron-Metabolism and the Relationship with the Immune Microenvironment of Gastric Cancer

2021 ◽  
Author(s):  
Fang Wen ◽  
Xiaoxue Chen ◽  
Wenjie Huang ◽  
Shuai Ruan ◽  
Suping Gu ◽  
...  
Keyword(s):  
Gastric Cancer ◽  
High Risk ◽  
Iron Metabolism ◽  
Risk Model ◽  
Cox Regression ◽  
Risk Group ◽  
Risk Groups ◽  
High Risk Group ◽  
Low Risk ◽  
Immune Microenvironment

Abstract Background: The diagnosis rate and mortality of gastric cancer (GC) are among the highest in the global, so it is of great significance to predict the survival time of GC patients. Ferroptosis and iron-metabolism make a critical impact on tumor development and are closely linked to the treatment of cancer and the prognosis of patients. However, the predictive value of the genes involved in ferroptosis and iron-metabolism in GC and their effects on immune microenvironment remain to be further clarified.Methods: In this study, the RNA sequence information and general clinical indicators of GC patients were acquired from the public databases. We first systematically screen out 134 DEGs and 13 PRGs related to ferroptosis and iron-metabolism. Then, we identified six PRDEGs (GLS2, MTF1, SLC1A5, SP1, NOX4, and ZFP36) based on the LASSO-penalized Cox regression analysis. The 6-gene prognostic risk model was established in the TCGA cohort and the GC patients were separated into the high- and the low-risk groups through the risk score median value. GEO cohort was used for verification. The expression of PRDEGs was verified by quantitative QPCR.Results: Our study demonstrated that patients in the low-risk group had a higher survival probability compared with those in high-risk group. In addition, univariate and multivariate Cox regression analyses confirmed that the risk score was an independent prediction parameter. The ROC curve analysis and nomogram manifested that the risk model had the high predictive ability and was more sensitive than general clinical features. Furthermore, compared with the high-risk group, the low-risk group had higher TMB and a longer 5-year survival period. In the immune microenvironment of GC, there were also differences in immune function and highly infiltrated immune cells between the two risk groups.Conclusions: The prognostic risk model based on the six genes associated with ferroptosis and iron-metabolism has a good performance for predicting the prognosis of patients with GC. The treatment of cancer by inducing tumor ferroptosis or mediating tumor iron-metabolism, especially combined with immunotherapy, provides a new possibility for individualized treatment of GC patients.

Final Risk Prediction Model for Neutropenic Complications in Patients Receiving Cancer Chemotherapy

Blood ◽  
2008 ◽  
Vol 112 (11) ◽  
pp. 1312-1312 ◽  
Author(s):  
Gary H. Lyman ◽  
Jeffrey Crawford ◽  
Nicole M. Kuderer ◽  
Debra Wolff ◽  
Eva Culakova ◽  
...  
Keyword(s):  
High Risk ◽  
Cancer Chemotherapy ◽  
Test Performance ◽  
Risk Model ◽  
Cumulative Risk ◽  
Risk Groups ◽  
Low Risk ◽  
Performance Characteristics ◽  
Risk Patients ◽  
Predicted Risk

Abstract Introduction: Neutropenic complications including severe neutropenia (SN) and febrile neutropenia (FN) represent major dose-limiting toxicities of cancer chemotherapy. A prospective study was undertaken to develop and validate a predictive model for neutropenic events in patients receiving cancer chemotherapy. The final risk model based on mature data is presented. Methods: Between 2002 and 2006, 4458 consenting patients starting a new chemotherapy regimen at 115 randomly selected community oncology practices throughout the United States were enrolled including 3760 with cancers of breast, lung, colorectum, ovary and malignant lymphoma receiving at least one cycle of treatment. Using a 2:1 random split sample methodlogy, a risk model for first-cycle SN or FN was derived and validated based on multivariate logistic regression analysis incorporating pretreatment variable information. The cumulative risk of events over the initial 120 days of treatment was estimated by the method of Kaplan and Meier. High and low risk groups were defined on the basis of the median predicted risk and model test performance characteristics were estimated. Results: Following adjustment for cancer type, important predictive factors included: older age, prior chemotherapy, abnormal hepatic or renal function, low pretreatment white blood count, immunosuppressive medications and planned relative dose intensity &gt;85% as well as use of several specific chemotherapeutic agents including anthracyclines, taxanes, alkylating agents, topoisomerase inhibitors, gemcitabine or vinorelbine. Lower risk of neutropenic complications were associated with primary prophylaxis with a colony-stimulating factor (CSF). Individual risk estimates based on the model ranged from 0–89% with mean and median of 19.2% and 10.1%, respectively. The model was associated with an R2 of 0.34 and demonstrated excellent discrimination with a c-statistic of 0.833 [95% CI: 0.813–0.852, P&lt;.001]. The model predicted risk of cycle 1 SN or FN in high and low risk groups was of 34% and 4%, respectively. The cumulative risk of FN over the initial 120 days was 20% in high risk patients and 5% in low risk patients. Model performance included sensitivity and specificity of 90% and 59%, respectively, with a model diagnostic odds ratio of 12.8 [95% CI: 9.3, 17.7]. Application of the model to the validation data set was associated with similar excellent discrimination and test performance characteristics. CSF prophylaxis applied to high risk patients was associated with significantly lower risk of FN over repeated cycles of chemotherapy [HR = 0.51; 95% CI: 0.35 – 0.75; P &lt;.0001]. Nearly two-thirds of patients classified as high risk but who did not receive primary CSF prophylaxis went on to receive secondary use during subsequent cycles. Discussion: Based on excellent test performance characteristics, the risk model identified patients with a cumulative incidence of FN of at least 20% who are candidates for targeted prophylaxis with a CSF. Further validation of this model in actual clinical practice is currently underway.

scholarly journals Genome Instability-related MiRNAs Predict Survival, Immune Landscape, and Immunotherapy Responses In Gastric Cancer

2021 ◽  
Author(s):  
Yaqiong Liu ◽  
Lin Cheng ◽  
Wei Huang ◽  
Xin Cheng ◽  
Weijun Peng
Keyword(s):  
Gastric Cancer ◽  
High Risk ◽  
Immune Cells ◽  
Genome Stability ◽  
Mutation Frequency ◽  
Genome Instability ◽  
Low Risk ◽  
The Cancer Genome Atlas ◽  
Disease Stage ◽  
Risk Patients

Abstract Backgroud Increasing evidence suggests that microRNAs (miRNAs) are involved in genome instability (GI) and drive the occurrence of tumors. However, the role of GI-related miRNAs in gastric cancer (GC) remains largely unknown. Herein, we developed a novel GI-related miRNA signature (GIMiSig) and further investigated its role in prognosis, the immune landscape, and immunotherapy responses in GC patients. Methods An analysis of somatic mutation data on 434 gastric cancer cases from The Cancer Genome Atlas (TCGA) database was performed, thereby generating genome stability (GS) and GI groups. By detecting differentially expressed miRNAs between the GS and GI groups that were associated with overall survival, 8 miRNAs were identified and used to construct the GIMiSig. Results The GIMiSig showed high accuracy in detecting GC patients. Using GIMiSig to stratify the patients into high- and low-risk subgroups to predict survival outperformed the use of regular clinical features such as age, gender, or disease stage. Patients with low risk had a more favorable survival time than those with high risk. More importantly, the high-risk patients were associated with decreased UBQLN4 expression, higher accumulation of immune cells, lower Titin (TTN) mutation frequency, worse immunotherapy efficacy, and cancer-associated pathways. Conversely, the low-risk patients were characterized by UBQLN4 overexpression, lower fraction of immune cells, higher TTN mutation frequency, better response to immunotherapy, and GI-related pathways. Conclusion In summary, we constructed a novel GIMiSig that could stratify GC patients into distinct risk groups that have different survival outcomes and immunotherapy efficacy. The results may provide new clues for improving GC outcomes.

scholarly journals A Novel Autophagy-Related lncRNA Gene Signature to Improve the Prognosis of Patients with Melanoma

2021 ◽  
Vol 2021 ◽  
pp. 1-12
Author(s):  
Yi Ding ◽  
Tian Li ◽  
Min Li ◽  
Tuersong Tayier ◽  
MeiLin Zhang ◽  
...  
Keyword(s):  
Overall Survival ◽  
High Risk ◽  
Risk Model ◽  
Risk Group ◽  
Clinical Information ◽  
Low Risk ◽  
Gene Set Enrichment Analysis ◽  
Positive Regulation ◽  
Risk Patients ◽  
Cox Analysis

Objective. Autophagy and long noncoding RNAs (lncRNAs) have been the focus of research on the pathogenesis of melanoma. However, the autophagy network of lncRNAs in melanoma has not been reported. The purpose of this study was to investigate the lncRNA prognostic markers related to melanoma autophagy and predict the prognosis of patients with melanoma. Methods. We downloaded RNA sequencing data and clinical information of melanoma from the Cancer Genome Atlas. The coexpression of autophagy-related genes (ARGs) and lncRNAs was analyzed. The risk model of autophagy-related lncRNAs was established by univariate and multivariate Cox regression analyses, and the best prognostic index was evaluated combined with clinical data. Finally, gene set enrichment analysis was performed on patients in the high- and low-risk groups. Results. According to the results of the univariate Cox analysis, only the overexpression of LINC00520 was associated with poor overall survival, unlike HLA-DQB1-AS1, USP30-AS1, AL645929, AL365361, LINC00324, and AC055822. The results of the multivariate Cox analysis showed that the overall survival of patients in the high-risk group was shorter than that recorded in the low-risk group ( p < 0.001 ). Moreover, in the receiver operating characteristic curve of the risk model we constructed, the area under the curve (AUC) was 0.734, while the AUC of T and N was 0.707 and 0.658, respectively. The Gene Ontology was mainly enriched with the positive regulation of autophagy and the activation of the immune system. The results of the Kyoto Encyclopedia of Genes and Genomes enrichment were mostly related to autophagy, immunity, and melanin metabolism. Conclusion. The positive regulation of autophagy may slow the transition from low-risk patients to high-risk patients in melanoma. Furthermore, compared with clinical information, the autophagy-related lncRNA risk model may better predict the prognosis of patients with melanoma and provide new treatment ideas.

scholarly journals Five Gene Probes Carry Most Of The Discriminatory Power Of The 70-Gene Risk Model In Multiple Myeloma

Blood ◽  
2013 ◽  
Vol 122 (21) ◽  
pp. 125-125
Author(s):  
Christoph Heuck ◽  
Pingping Qu ◽  
Frits van Rhee ◽  
Sarah Waheed ◽  
Saad Z Usmani ◽  
...  
Keyword(s):  
High Risk ◽  
Research Funding ◽  
Risk Model ◽  
Progression Free Survival ◽  
Dna Probes ◽  
Low Risk ◽  
Free Survival ◽  
The Us ◽  
Risk Patients ◽  
Total Therapy

Abstract Gene expression profiling (GEP) reliably predicts overall and progression free survival in multiple myeloma. Driven by the concept that therapy will reveal biology, we applied the GEP70 risk model to 56 patients enrolled in Total Therapy 6 (TT6), a phase 2 trial for previously treated patients. One year survival estimates were 62% vs.97%, p<0.0001, Figure 1A). To investigate whether fewer than the 70 genes could predict this difference in outcomes, the probe sets of the GEP70 risk model were ranked by p-values, based on univariate Cox regression analysis for OS. The five probe sets with the smallest P values (corresponding to genes ENO1, FABP5, TRIP13, TAGLN2, and RFC4) were combined to create a continuous score (Figure1B). Association of several of these genes with different cancers has previously been reported by others. We re-trained this 5 gene model (GEP5) on a dataset of 275 uniformly treated patients on Total Therapy 3A (TT3A) and identified a new optimal cutoff of 10.68. We validated this new cutoff with patients enrolled in Total Therapy 2 (TT2) (n=351) and Total Therapy 3B (TT3B) (n=166). For TT2 patients, the dataset from which the GEP70 model was developed, clinical outcomes of the GEP5defined low risk patients were very similar to the GEP70 defined low risk patients. Survival estimates were higher for GEP5-defined high risk than for GEP70 high risk patients (5-year estimated OS: 40%, GEP5; 28%, GEP70; 5-year estimated PFS: 26%, GEP5; 15%, GEP70) (Figure 2A and 2B). This was also seen in both TT2 treatment arms. For the second validation cohort (TT3B), GEP5 and GEP70 risk distinction were similar to the TT3A discovery cohort (Figures 2C and 2D). On multivariate analysis, the GEP5-defined high-risk designation was the most adverse variable for PFS, with an estimated hazard ratio of 3.44 (95% CI: 2.02-5.86), whereas the GEP70 model was selected first for OS. (Table 1).Table 1.Multivariate stepwise Cox regression analysis performed on the TT3B validation setOverall survivalProgression-free survivalVariablen/N (%)HR (95% CI)P-valueHR (95% CI)P-valueMultivariateGEP70 high-risk36/159 (23%)4.45 (2.47, 8.02)<.001B2M > 5.5 mg/L49/159 (31%)1.73 (1.01, 2.95)0.045GEP5 high-risk42/159 (26%)3.44 (2.02, 5.86)<.001 Applied to the publicly available dataset from the HOVON group, GEP5 identified a high risk group with a 3-year estimate OS survival of 52% compared to 75% for the low risk group (p<0.001). TT4 and TT5 are phase 2 trials for previously untreated GEP70 defined low-risk and high-risk patients, respectively. GEP5 identified in TT4 a subset (17/303) of high risk patients with significantly worse 3-year estimated OS (69% vs. 86%, p=0.03), and in TT5 GEP5 identifies a low-risk subset (22/57) of patients with significantly better 3-year estimate OS (94% vs. 59%, p=0.01). Recently a large-scale proteomics experiment involving 85 patients with MM identified ENO1, FABP5, and TAGLN2 among a set of 24 proteins that are associated with short OS. It was further shown that gene expression levels correlated closely with protein abundance. In summary, we have identified 5 genes that have the greatest influence on GEP defined risk. The GEP5 score maintains prognostic power even in patients who have been risk stratified using other risk models. The correlation of expression at both mRNA and protein levels indicate that the genes identified in GEP5 are not simply an artifact of the microarray methodology, but rather supports their biologic relevance. This simplified risk model with a reduced number of genes has the potential to open molecular risk testing to a larger audience. Figure 1. Figure 1. Overall Survival in TT6 according to A) GEP70 risk score and B) GEP5 risk score Figure 2. Figure 2. Overall survival (left panels) and progression-free survival (right panels) according to GEP5 risk score in A) TT3A training set set, B) TT2 test set and C) a second test set TT3B Figure 3. Figure 3. Overall survival (left panels) and progression-free survival (right panels) according to GEP5 in A) the publicly available HOVON dataset B) TT4, for previously untreated GEP70 defined low-risk myeloma and C) TT5, for previously untreated GEP70 defined high-risk myeloma Disclosures: van Rhee: Jansen & Jansen: Research Funding. Usmani:Celgene: Consultancy, Research Funding, Speakers Bureau; Onyx: Research Funding, Speakers Bureau. Epstein:University of Arkansas for Medical Sciences: Co-inventor of the DNA probes for FISH of IGHC/IGHV (14q32), MMSET/FGFR3 (4p16), CCND3 (6p21), CCND1 (11q13), MAF (16q23), and MAFB (20q12) loci, sub. to the US Patent & Trademark Office as Prov. App# 61/726,327: Methods of Detecting 14q32 Translocations, Co-inventor of the DNA probes for FISH of IGHC/IGHV (14q32), MMSET/FGFR3 (4p16), CCND3 (6p21), CCND1 (11q13), MAF (16q23), and MAFB (20q12) loci, sub. to the US Patent & Trademark Office as Prov. App# 61/726,327: Methods of Detecting 14q32 Translocations Patents & Royalties. Zhang:University of Arkansas for Medical Sciences: Co-inventor of the DNA probes for FISH of IGHC/IGHV (14q32), MMSET/FGFR3 (4p16), CCND3 (6p21), CCND1 (11q13), MAF (16q23), and MAFB (20q12) loci, sub. to the US Patent & Trademark Office as Prov. App# 61/726,327: Methods of Detecting 14q32 Translocations, Co-inventor of the DNA probes for FISH of IGHC/IGHV (14q32), MMSET/FGFR3 (4p16), CCND3 (6p21), CCND1 (11q13), MAF (16q23), and MAFB (20q12) loci, sub. to the US Patent & Trademark Office as Prov. App# 61/726,327: Methods of Detecting 14q32 Translocations Patents & Royalties. Barlogie:Celgene: Consultancy, Honoraria, Research Funding; Myeloma Health, LLC: Patents & Royalties.

scholarly journals Genome Instability-Related miRNAs Predict Survival, Immune Landscape, and Immunotherapy Responses in Gastric Cancer

2021 ◽  
Vol 2021 ◽  
pp. 1-27
Author(s):  
Yaqiong Liu ◽  
Lin Cheng ◽  
Wei Huang ◽  
Xin Cheng ◽  
Weijun Peng ◽  
...  
Keyword(s):  
Gastric Cancer ◽  
High Risk ◽  
Immune Cells ◽  
Genome Stability ◽  
Mutation Frequency ◽  
Genome Instability ◽  
Low Risk ◽  
The Cancer Genome Atlas ◽  
Disease Stage ◽  
Risk Patients

Background. Increasing evidence suggests that microRNAs (miRNAs) are involved in genome instability (GI) and drive the occurrence of tumors. However, the role of GI-related miRNAs in gastric cancer (GC) remains largely unknown. Herein, we developed a novel GI-related miRNA signature (GIMiSig) and further investigated its role in prognosis, the immune landscape, and immunotherapy responses in GC patients. Methods. An analysis of somatic mutation data on 434 gastric cancer cases from The Cancer Genome Atlas (TCGA) database was performed, thereby generating genome stability (GS) and GI groups. By detecting differentially expressed miRNAs between the GS and GI groups that were associated with overall survival, 8 miRNAs were identified and used to construct the GIMiSig. Results. The GIMiSig showed high accuracy in detecting GC patients. Using GIMiSig to stratify the patients into the high- and low-risk subgroups to predict survival outperformed the use of regular clinical features such as age, gender, or disease stage. Patients with low risk had a more favorable survival time than those with high risk. More importantly, the high-risk patients were associated with decreased UBQLN4 expression, higher accumulation of immune cells, lower Titin (TTN) mutation frequency, worse immunotherapy efficacy, and cancer-associated pathways. Conversely, the low-risk patients were characterized by UBQLN4 overexpression, lower fraction of immune cells, higher TTN mutation frequency, better response to immunotherapy, and GI-related pathways. Conclusion. In summary, we constructed a novel GIMiSig that could stratify GC patients into distinct risk groups that have different survival outcomes and immunotherapy efficacy. The results may provide new clues for improving GC outcomes.

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