Bioinformatics analysis of esophageal cancer unveils an integrated mRNA‑lncRNA signature for predicting prognosis

Lan, Tian; Xiao, Zunqiang; Luo, Hua; Su, Kunlun; Yang, Ouou; Zhan, Chengni; Lu, Yunyan

doi:10.3892/ol.2019.11208

Cited by 4 publications

(2 citation statements)

References 35 publications

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“…Moreover, these genes may serve as viable targets for treatment strategies. A significant difference in prognosis between risk groups is a prerequisite for group stratification [ 40 ]. A higher risk group had a worse prognosis compared to a low-risk group in this study.…”

Section: Discussionmentioning

confidence: 99%

Risk model based on genes regulating the response of tumor cells to T-cell-mediated killing in esophageal squamous cell carcinoma

Zhang,

Yu,

Zhou

et al. 2024

Aging

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Immune checkpoint inhibitors (ICIs) represent a promising therapeutic approach for esophageal squamous cell carcinoma (ESCC). However, the subpopulations of ESCC patients expected to benefit from ICIs have not been clearly defined. The anti-tumor cytotoxic activity of T cells is an important pharmacological mechanism of ICIs. In this study, the prognostic value of the genes regulating tumor cells to T cell-mediated killing (referred to as GRTTKs) in ESCC was explored by using a comprehensive bioinformatics approach. Training and validation datasets were obtained from The Cancer Genome Atlas (TCGA) and Gene Expression Omnibus (GEO), respectively. A prognostic risk scoring model was developed by integrating prognostic GRTTKs from TCGA and GEO datasets using a ridge regression algorithm. Patients with ESCC were divided into high- and low-risk groups based on eight GRTTKs ( EIF4H , CDK2 , TCEA1 , SPTLC2 , TMEM209 , RGP1 , EIF3D , and CAPZA3 ) to predict overall survival in the TCGA cohort. Using Kaplan-Meier curves, receiver operating characteristic curves, and C-index analysis, the high reliability of the prognostic risk-scoring model was certified. The model scores served as independent prognostic factors, and combining clinical staging with risk scoring improved the predictive value. Patients in the high-risk group exhibited abundant immune cell infiltration, including immune checkpoint expression, antigen presentation capability, immune cycle gene expression, and high tumor inflammation signature scores. The high-risk group exhibited a greater response to immunotherapy and neoadjuvant chemotherapy than the low-risk group. Drug sensitivity analysis demonstrated lower IC50 for AZD6244 and PD.0332991 in high-risk groups and lower IC50 for cisplatin, ATRA, QS11, and vinorelbine in the low-risk group. Furthermore, the differential expression of GRTTK-related signatures including CDK2, TCEA1, and TMEM209 were verified in ESCC tissues and paracancerous tissues. Overall, the novel GRTTK-based prognostic model can serve as indicators to predict the survival status and immunotherapy response of patients with ESCC, thereby providing guidance for the development of personalized treatment strategies.

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Section: Discussionmentioning

confidence: 99%

Risk model based on genes regulating the response of tumor cells to T-cell-mediated killing in esophageal squamous cell carcinoma

Zhang,

Yu,

Zhou

et al. 2024

Aging

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“…EC is the most common malignant tumor of the digestive tract and has a poor prognosis. Studies have established several novel biomarkers to determine the prognosis of EC patients, such as miRNA signatures 7,8 , autophagy-related signatures 9 , m6A RNA methylation regulator-based signatures 10 , integrated mRNA-lncRNA signatures 11 , epigenetic signatures 12 , lymph node metastasis-associated gene signatures 13 , urinary metabolomic signatures 14 , and immune-related gene signatures 15 . Although RBPrelated signatures have been analyzed for a variety of cancers [16][17][18][19][20][21] , related analyses for EC are rare.…”

Section: Discussionmentioning

confidence: 99%

Genome-Wide Profiling of a Prognostic RNA-Binding Protein Signature in Esophageal Cancer

Zhang

Yang

et al. 2022

Preprint

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Background the roles of RNA-binding proteins (RBPs) in esophageal cancer (EC) need elucidation. Materials and methods The gene expression profiles and clinical data of patients with EC were downloaded from the Xena database. Candidate genes were obtained by taking the union of RBP genes, KEGG pathway-related genes and differentially expressed RBP genes from cluster analysis. Hub genes were extracted by PPI network construction. Result We built a Cox proportional hazards regression model with 7 prognostic RBPs (TRMT2A, PDHA1, MPRIP, KRI1, IL17A, HSPA1A and HIST1H4J). The risk score of each patient in the internal and external dataset cohorts was calculated and then the patients were divided into two groups based on the median. There were significant differences between the RBP risk score and response to chemotherapy, with low-risk patients being more likely to achieve CR. Finally, the risk score was revealed to be significantly related to OS by univariate and multivariate analyses, and pathological stage could also be used independently to predict the prognosis of EC. Conclusion Our study indicated that the RBP signature could serve as a prognostic biomarker of EC and provide new insight into chemoresistance in EC patients.

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Survival Risk Prediction of Esophageal Cancer Based on Self-Organizing Maps Clustering and Support Vector Machine Ensembles

et al. 2020

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This article provides a method based on self-organizing maps (SOM) neural network clustering and support vector machine (SVM) ensembles to predict the survival risk levels of esophageal cancer. Nine blood indexes related to patient survival are found by using SOM clustering method. Two critical thresholds for survival are found by plotting the receiver operating characteristic (ROC) curve twice, and the lifetime is divided into three risk levels. Using the SVM method, patients' risk levels are predicted and assessed. Four kernel functions of SVM are compared, and the prediction effect of RBF kernel function is better than other kernel functions. The parameters of SVM are optimized by using genetic algorithm (GA), particle swarm algorithm (PSO) and artificial bee colony (ABC) algorithm. Experimental results show that the prediction accuracies are improved by using optimization algorithms. After comparison, ABC-SVM has better prediction results than GA-SVM and PSO-SVM with a high prediction rate and fast running time. INDEX TERMS artificial bee colony, genetic algorithm, particle swarm optimization, self-organizing maps, support vector machine.

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Bioinformatics analysis of esophageal cancer unveils an integrated mRNA‑lncRNA signature for predicting prognosis

Cited by 4 publications

References 35 publications

Risk model based on genes regulating the response of tumor cells to T-cell-mediated killing in esophageal squamous cell carcinoma

Risk model based on genes regulating the response of tumor cells to T-cell-mediated killing in esophageal squamous cell carcinoma

Genome-Wide Profiling of a Prognostic RNA-Binding Protein Signature in Esophageal Cancer

Survival Risk Prediction of Esophageal Cancer Based on Self-Organizing Maps Clustering and Support Vector Machine Ensembles

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