Immune-Related Nine-MicroRNA Signature for Predicting the Prognosis of Gastric Cancer

Recent findings have demonstrated the superiority and utility of microRNAs (miRNAs) as new biomarkers for cancer diagnosis, therapy, and prognosis. In this study, to explore the prognostic value of immune-related miRNAs in gastric cancer (GC), we analyzed the miRNA-expression profiles of 389 patients with GC, using data deposited in The Cancer Genome Atlas database. Using a forward- and backward-variable selection and multivariate Cox regression analyses model, we identified a nine-miRNA signature (the “ImmiRSig,” consisting of miR-125b-5p, miR-99a-3p, miR-145-3p, miR-328-3p, miR-133a-5p, miR-1292-5p, miR-675-3p, miR-92b-5p, and miR-942-3p) in the training cohort that enabled the division of patients into high- and low-risk groups with significantly different survival rates. The ImmiRSig was successfully validated with an independent test cohort of 193 GC patients. Univariate and multivariate Cox regression analyses indicated that the ImmiRSig would serve as an independent prognostic factor after adjusting for other clinical covariates. Pending further prospective validation, the identified ImmiRSig appears to have significant clinical importance in terms of improving outcome predictions and guiding personalized treatment for patients with GC. Finally, significant associations between the ImmiRSig and the half-maximal inhibitory concentrations of chemotherapeutic agents were observed, suggesting that ImmiRSig may predict the clinical efficacy of chemotherapy.

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A novel prognostic model based on epithelial-mesenchymal transition-related genes predicts patient survival in gastric cancer

World Journal of Surgical Oncology ◽

10.1186/s12957-021-02329-9 ◽

2021 ◽

Vol 19 (1) ◽

Author(s):

Wanting Song ◽

Yi Bai ◽

Jialin Zhu ◽

Fanxin Zeng ◽

Chunmeng Yang ◽

...

Keyword(s):

Gene Expression ◽

Gastric Cancer ◽

Risk Model ◽

Cox Regression ◽

Epithelial Mesenchymal Transition ◽

Risk Groups ◽

The Cancer Genome Atlas ◽

Critical Function ◽

Mesenchymal Transition ◽

Patient Prognosis

Abstract Background Gastric cancer (GC) represents a major malignancy and is the third deathliest cancer globally. Several lines of evidence indicate that the epithelial-mesenchymal transition (EMT) has a critical function in the development of gastric cancer. Although plentiful molecular biomarkers have been identified, a precise risk model is still necessary to help doctors determine patient prognosis in GC. Methods Gene expression data and clinical information for GC were acquired from The Cancer Genome Atlas (TCGA) database and 200 EMT-related genes (ERGs) from the Molecular Signatures Database (MSigDB). Then, ERGs correlated with patient prognosis in GC were assessed by univariable and multivariable Cox regression analyses. Next, a risk score formula was established for evaluating patient outcome in GC and validated by survival and ROC curves. In addition, Kaplan-Meier curves were generated to assess the associations of the clinicopathological data with prognosis. And a cohort from the Gene Expression Omnibus (GEO) database was used for validation. Results Six EMT-related genes, including CDH6, COL5A2, ITGAV, MATN3, PLOD2, and POSTN, were identified. Based on the risk model, GC patients were assigned to the high- and low-risk groups. The results revealed that the model had good performance in predicting patient prognosis in GC. Conclusions We constructed a prognosis risk model for GC. Then, we verified the performance of the model, which may help doctors predict patient prognosis.

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Identification of Tumorigenic and Prognostic Biomarkers in Colorectal Cancer Based on microRNA Expression Profiles

BioMed Research International ◽

10.1155/2020/7136049 ◽

2020 ◽

Vol 2020 ◽

pp. 1-8

Author(s):

Yuntao Shi ◽

Yingying Zhuang ◽

Jialing Zhang ◽

Mengxue Chen ◽

Shangnong Wu

Keyword(s):

Target Genes ◽

Cox Regression ◽

Expression Profiles ◽

Survival Rates ◽

Enrichment Analysis ◽

Functional Enrichment Analysis ◽

Risk Groups ◽

Normal Mucosa ◽

Microrna Expression ◽

Functional Enrichment

Objective. Although noncoding RNAs, especially the microRNAs, have been found to play key roles in CRC development in intestinal tissue, the specific mechanism of these microRNAs has not been fully understood. Methods. GEO and TCGA database were used to explore the microRNA expression profiles of normal mucosa, adenoma, and carcinoma. And the differential expression genes were selected. Computationally, we built the SVM model and multivariable Cox regression model to evaluate the performance of tumorigenic microRNAs in discriminating the adenomas from normal tissues and risk prediction. Results. In this study, we identified 20 miRNA biomarkers dysregulated in the colon adenomas. The functional enrichment analysis showed that MAPK activity and MAPK cascade were highly enriched by these tumorigenic microRNAs. We also investigated the target genes of the tumorigenic microRNAs. Eleven genes, including PIGF, TPI1, KLF4, RARS, PCBP2, EIF5A, HK2, RAVER2, HMGN1, MAPK6, and NDUFA2, were identified to be frequently targeted by the tumorigenic microRNAs. The high AUC value and distinct overall survival rates between the two risk groups suggested that these tumorigenic microRNAs had the potential of diagnostic and prognostic value in CRC. Conclusions. The present study revealed possible mechanisms and pathways that may contribute to tumorigenesis of CRC, which could not only be used as CRC early detection biomarkers, but also be useful for tumorigenesis mechanism studies.

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A lipid metabolism-related genes prognosis biomarker associated with the tumor immune microenvironment in colorectal carcinoma

BMC Cancer ◽

10.1186/s12885-021-08902-5 ◽

2021 ◽

Vol 21 (1) ◽

Author(s):

Chao Yang ◽

Shuoyang Huang ◽

Fengyu Cao ◽

Yongbin Zheng

Keyword(s):

Lipid Metabolism ◽

Cox Regression ◽

Malignant Tumors ◽

Expression Profiles ◽

Risk Groups ◽

Clinical Parameter ◽

Gene Expression Omnibus ◽

Metabolic Reprogramming ◽

The Cancer Genome Atlas ◽

Immune Microenvironment

Abstract Background and aim Lipid metabolic reprogramming is considered to be a new hallmark of malignant tumors. The purpose of this study was to explore the expression profiles of lipid metabolism-related genes (LMRG) in colorectal cancer (CRC). Methods The lipid metabolism statuses of 500 CRC patients from the Cancer Genome Atlas (TCGA) and 523 from the Gene Expression Omnibus (GEO GSE39582) database were analyzed. The risk signature was constructed by univariate Cox regression and least absolute shrinkage and selection operator (LASSO) Cox regression. Results A novel four-LMRG signature (PROCA1, CCKBR, CPT2, and FDFT1) was constructed to predict clinical outcomes in CRC patients. The risk signature was shown to be an independent prognostic factor for CRC and was associated with tumour malignancy. Principal components analysis demonstrated that the risk signature could distinguish between low- and high-risk patients. There were significantly differences in abundances of tumor-infiltrating immune cells and mutational landscape between the two risk groups. Patients in the low-risk group were more likely to have higher tumor mutational burden, stem cell characteristics, and higher PD-L1 expression levels. Furthermore, a genomic-clinicopathologic nomogram was established and shown to be a more effective risk stratification tool than any clinical parameter alone. Conclusions This study demonstrated the prognostic value of LMRG and showed that they may be partially involved in the suppressive immune microenvironment formation.

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A Prognostic Nomogram Model Based on mRNA Expression of DNA Methylation-Driven Genes for Gastric Cancer

Frontiers in Oncology ◽

10.3389/fonc.2020.584733 ◽

2020 ◽

Vol 10 ◽

Author(s):

Zuhua Chen ◽

Bo Liu ◽

Minxiao Yi ◽

Hong Qiu ◽

Xianglin Yuan

Keyword(s):

Gastric Cancer ◽

Dna Methylation ◽

Mrna Expression ◽

Risk Model ◽

Cox Regression ◽

Risk Group ◽

Expression Profiles ◽

The Cancer Genome Atlas ◽

Clinicopathological Parameters ◽

Dna Hypomethylation

PurposeThe exploration and interpretation of DNA methylation-driven genes might contribute to molecular classification, prognostic prediction and therapeutic choice. In this study, we built a prognostic risk model via integrating analysis of the transcriptome and methylation profile for patients with gastric cancer (GC).MethodsThe mRNA expression profiles, DNA methylation profiles and corresponding clinicopathological information of 415 GC patients were downloaded from The Cancer Genome Atlas (TCGA). Differential expression and correlation analysis were performed to identify DNA methylation-driven genes. The candidate genes were selected by univariate Cox regression analyses followed by the least absolute shrinkage and selection operator (LASSO) regression. A prognostic risk nomogram model was then built together with clinicopathological parameters.Results5 DNA methylation-driven genes (CXCL3, F5, GNAI1, GAMT and GHR) were identified by integrated analyses and selected to construct the prognostic risk model with clinicopathological parameters. High expression and low DNA hypermethylation of F5, GNAI1, GAMT and GHR, as well as low expression and high DNA hypomethylation of CXCL3 were significantly associated with poor prognosis rates, respectively. The high-risk group showed a significantly shorter prognosis than the low-risk group in the TCGA dataset (HR = 0.212, 95% CI = 0.139–0.322, P = 2e-15). The final nomogram model showed high predictive efficiency and consistency in the training and validation group.ConclusionWe construct and validate a prognostic nomogram model for GC based on five DNA methylation-driven genes with high performance and stability. This nomogram model might be a powerful tool for prognosis evaluation in the clinic and also provided novel insights into the epigenetics in GC.

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Identification and validation of a novel ferroptosis-related gene model for predicting the prognosis of gastric cancer patients

PLoS ONE ◽

10.1371/journal.pone.0254368 ◽

2021 ◽

Vol 16 (7) ◽

pp. e0254368

Author(s):

Gang Liu ◽

Jian-ying Ma ◽

Gang Hu ◽

Huan Jin

Keyword(s):

Gene Expression ◽

Gastric Cancer ◽

Risk Score ◽

Cox Regression ◽

Predictive Accuracy ◽

Expression Profiles ◽

Critical Role ◽

Clinicopathological Features ◽

The Cancer Genome Atlas ◽

Risk Scores

Background Ferroptosis is a novel form of regulated cell death that plays a critical role in tumorigenesis. The purpose of this study was to establish a ferroptosis-associated gene (FRG) signature and assess its clinical outcome in gastric cancer (GC). Methods Differentially expressed FRGs were identified using gene expression profiles from The Cancer Genome Atlas (TCGA) and Gene Expression Omnibus (GEO) database. Univariate and least absolute shrinkage and selection operator (LASSO) Cox regression analyses were performed to construct a prognostic signature. The model was validated using an independent GEO dataset, and a genomic-clinicopathologic nomogram integrating risk scores and clinicopathological features was established. Results An 8-FRG signature was constructed to calculate the risk score and classify GC patients into two risk groups (high- and low-risk) according to the median value of the risk score. The signature showed a robust predictive capacity in the stratification analysis. A high-risk score was associated with advanced clinicopathological features and an unfavorable prognosis. The predictive accuracy of the signature was confirmed using an independent GSE84437 dataset. Patients in the two groups showed different enrichment of immune cells and immune-related pathways. Finally, we established a genomic-clinicopathologic nomogram (based on risk score, age, and tumor stage) to predict the overall survival (OS) of GC patients. Conclusions The novel FRG signature may be a reliable tool for assisting clinicians in predicting the OS of GC patients and may facilitate personalized treatment.

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A novel survival model based on a Ferroptosis-related gene signature for predicting overall survival in bladder cancer

BMC Cancer ◽

10.1186/s12885-021-08687-7 ◽

2021 ◽

Vol 21 (1) ◽

Author(s):

Yingchun Liang ◽

Fangdie Ye ◽

Chenyang Xu ◽

Lujia Zou ◽

Yun Hu ◽

...

Keyword(s):

Overall Survival ◽

Bladder Cancer ◽

Prognostic Value ◽

Cox Regression ◽

Risk Group ◽

Expression Profiles ◽

Risk Groups ◽

Gene Set Enrichment Analysis ◽

The Cancer Genome Atlas ◽

Prognosis Prediction

Abstract Background The effective treatment and prognosis prediction of bladder cancer (BLCA) remains a medical problem. Ferroptosis is an iron-dependent form of programmed cell death. Ferroptosis is closely related to tumour occurrence and progression, but the prognostic value of ferroptosis-related genes (FRGs) in BLCA remains to be further clarified. In this study, we identified an FRG signature with potential prognostic value for patients with BLCA. Methods The corresponding clinical data and mRNA expression profiles of BLCA patients were downloaded from The Cancer Genome Atlas (TCGA). Univariate Cox regression was used to extract FRGs related to survival time, and a Cox regression model was used to construct a multigene signature. Both principal component analysis (PCA) and single-sample gene set enrichment analysis (ssGSEA) were performed for functional annotation. Results Clinical traits were combined with FRGs, and 15 prognosis-related FRGs were identified by Cox regression. High expression of CISD1, GCLM, CRYAB, SLC7A11, TFRC, ACACA, ZEB1, SQLE, FADS2, ABCC1, G6PD and PGD was related to poor survival in BLCA patients. Multivariate Cox regression was used to construct a prognostic model with 7 FRGs that divided patients into two risk groups. Compared with that in the low-risk group, the overall survival (OS) of patients in the high-risk group was significantly lower (P < 0.001). In multivariate regression analysis, the risk score was shown to be an independent predictor of OS (HR = 1.772, P < 0.01). Receiver operating characteristic (ROC) curve analysis verified the predictive ability of the model. In addition, the two risk groups displayed different immune statuses in ssGSEA and different distributed patterns in PCA. Conclusion Our research suggests that a new gene model related to ferroptosis can be applied for the prognosis prediction of BLCA. Targeting FRGs may be a treatment option for BLCA.

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Necroptosis-Related lncRNAs: Predicting Prognosis and the Distinction between the Cold and Hot Tumors in Gastric Cancer

Journal of Oncology ◽

10.1155/2021/6718443 ◽

2021 ◽

Vol 2021 ◽

pp. 1-16

Author(s):

Zirui Zhao ◽

Haohan Liu ◽

Xingyu Zhou ◽

Deliang Fang ◽

Xinde Ou ◽

...

Keyword(s):

Gastric Cancer ◽

Cox Regression ◽

Principal Component ◽

Risk Groups ◽

Tissue Expression ◽

Individual Therapy ◽

The Cancer Genome Atlas ◽

Operating Characteristics ◽

Kaplan Meier ◽

Good Concordance

Background. In the face of poor prognosis and immunotherapy failure of gastric cancer (GC), this project tried to find new potential biomarkers for predicting prognosis and precision medication to ameliorate the situation. Methods. To form synthetic matrices, we retrieved stomach adenocarcinoma transcriptome data from Genotype-Tissue Expression Project (GTEx) and The Cancer Genome Atlas (TCGA). Necroptosis-related prognostic lncRNA was identified by coexpression analysis and univariate Cox regression. Then we performed the least absolute shrinkage and selection operator (LASSO) to construct the necroptosis-related lncRNA model. Next, the Kaplan–Meier analysis, time-dependent receiver operating characteristics (ROC), univariate Cox (uni-Cox) regression, multivariate Cox (multi-Cox) regression, nomogram, and calibration curves were made to verify and evaluate the model. Gene set enrichment analyses (GSEA), principal component analysis (PCA), immune analysis, and prediction of the half-maximal inhibitory concentration (IC50) in risk groups were also analyzed. For further discussing immunotherapy between the cold and hot tumors, we divided the entire set into two clusters based on necroptosis-related lncRNAs. Results. We constructed a model with 16 necroptosis-related lncRNAs. In the model, we found the calibration plots showed a good concordance with the prognosis prediction. The area’s 1-, 2-, and 3-year OS under the ROC curve (AUC) were 0.726, 0.763, and 0.770, respectively. Risk groups could be a guide of systemic treatment because of significantly different IC50 between risk groups. Above all, clusters could help distinguish between the cold and hot tumors effectively and contribute to precise mediation. Cluster 2 was identified as the hot tumor and more susceptible to immunotherapeutic drugs. Conclusion. The results of this project supported that necroptosis-related lncRNAs could predict prognosis and help make a distinction between the cold and hot tumors for improving individual therapy in GC.

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A Novel Prognostic Model Based on Ferroptosis-Related Gene Signature for Bladder Cancer

Frontiers in Oncology ◽

10.3389/fonc.2021.686044 ◽

2021 ◽

Vol 11 ◽

Author(s):

Libo Yang ◽

Chunyan Li ◽

Yang Qin ◽

Guoying Zhang ◽

Bin Zhao ◽

...

Keyword(s):

Bladder Cancer ◽

Risk Score ◽

Prognostic Model ◽

Cox Regression ◽

Expression Profiles ◽

Enrichment Analysis ◽

Risk Groups ◽

Gene Set Enrichment Analysis ◽

Regression Analyses ◽

Model Based

BackgroundBladder cancer (BC) is a molecular heterogeneous malignant tumor; the treatment strategies for advanced-stage patients were limited. Therefore, it is vital for improving the clinical outcome of BC patients to identify key biomarkers affecting prognosis. Ferroptosis is a newly discovered programmed cell death and plays a crucial role in the occurrence and progression of tumors. Ferroptosis-related genes (FRGs) can be promising candidate biomarkers in BC. The objective of our study was to construct a prognostic model to improve the prognosis prediction of BC.MethodsThe mRNA expression profiles and corresponding clinical data of bladder urothelial carcinoma (BLCA) patients were downloaded from The Cancer Genome Atlas (TCGA) and Gene Expression Omnibus (GEO) databases. FRGs were identified by downloading data from FerrDb. Differential analysis was performed to identify differentially expressed genes (DEGs) related to ferroptosis. Univariate and multivariate Cox regression analyses were conducted to establish a prognostic model in the TCGA cohort. BLCA patients from the GEO cohort were used for validation. Gene ontology (GO), Kyoto Encyclopedia of Genes and Genomes (KEGG), and single-sample gene set enrichment analysis (ssGSEA) were used to explore underlying mechanisms.ResultsNine genes (ALB, BID, FADS2, FANCD2, IFNG, MIOX, PLIN4, SCD, and SLC2A3) were identified to construct a prognostic model. Patients were classified into high-risk and low-risk groups according to the signature-based risk score. Receiver operating characteristic (ROC) and Kaplan–Meier (K–M) survival analysis confirmed the superior predictive performance of the novel survival model based on the nine-FRG signature. Multivariate Cox regression analyses showed that risk score was an independent risk factor associated with overall survival (OS). GO and KEGG enrichment analysis indicated that apart from ferroptosis-related pathways, immune-related pathways were significantly enriched. ssGSEA analysis indicated that the immune status was different between the two risk groups.ConclusionThe results of our study indicated that a novel prognostic model based on the nine-FRG signature can be used for prognostic prediction in BC patients. FRGs are potential prognostic biomarkers and therapeutic targets.

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Development and Validation of a Six-lncRNA Prognostic Signature in Gastric Cancer

10.21203/rs.3.rs-842112/v1 ◽

2021 ◽

Author(s):

Huihui Zeng ◽

Ai tao Nai ◽

Feng Ma ◽

SHOAIB BASHIR ◽

Yin Li ◽

...

Keyword(s):

Gastric Cancer ◽

Target Genes ◽

Cox Regression ◽

Expression Profiles ◽

Enrichment Analysis ◽

The Cancer Genome Atlas ◽

Expression Level ◽

Training Dataset ◽

Lasso Regression ◽

Prognostic Signature

Abstract Background: Gastric cancer (GC) has been a leading cause of cancer-related mortality for many years. It is thought that long noncoding RNAs (lncRNAs) can play a significant role in GC. This study aimed to construct a powerful six-lncRNA signature as a prognostic biomarker for GC patients.Methods: Based on The Cancer Genome Atlas (TCGA), the expression profiles of lncRNAs and the corresponding clinical data of GC patients were obtained. Cox regression and the least absolute shrinkage and selection operator (LASSO) regression model were used to identify the prognostic lncRNA signature. A total of 337 patients were included in the combined dataset (N = 337), which was divided into a training dataset (N= 169) and a test dataset (N = 168). The reliability of the lncRNA prognostic signature was validated in three datasets.Results: A six-lncRNA prognostic signature was constructed to predict the overall survival (OS) of GC patients. The signature had better discriminability than clinical characteristics. The prognostic risk score was as follows: (expression level of RP11-284F21.7×-0.243981) + (expression level of RP11-432J22.2×-0.502378) + (expression level of RP4-584D14.5×-0.447878) + (expression level of AC093850.2×0.261822) + (expression level of AP000695.6 ×0.654318) + (expression level of AC098973.2× 0.406603). In addition, the signature was confirmed to be a significant predictor for predicting the OS. The nomogram model precisely predicted the OS of GC. Enrichment analysis indicated that the signature was mainly enriched for extracellular matrix-related functions and tumor signaling pathways. The target genes IGFBP7, VCAN, and COL1A1 had prognostic value in GC. AC098973.2 and RP11-284F21.7 was verified for the first time in GC tissues and cell lines.Conclusions: The six-lncRNA prognostic signature could predict the OS and has high clinical application value in GC.

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Autophagy-related signature as Indicators for the Prognosis of Hepatocellular carcinoma

10.21203/rs.3.rs-32431/v1 ◽

2020 ◽

Author(s):

Wen Ye ◽

Zhehao Shi ◽

Zhongjing Zhang ◽

Yi Zhou ◽

Bicheng Chen ◽

...

Keyword(s):

Hepatocellular Carcinoma ◽

Liver Cancer ◽

Regression Model ◽

Cox Regression ◽

Expression Profiles ◽

Risk Groups ◽

Cancer Genome ◽

The Cancer Genome Atlas ◽

Prognostic Signature ◽

Cox Regression Analysis

Abstract Background Hepatocellular carcinoma (HCC) is the most common and deadly type of liver cancer. Autophagy is the process of transporting damaged or aging cellular components into lysosomes for digestion and degradation. There is an accumulative evidence implies that autophagy is a key factor of the progression of cancer. The aim of this study was to determine a panel of a novel autophagy-related prognostic marker for liver cancer. Methods We conducted a comprehensive analysis of ARGs expression profiles and corresponding clinical information based on The Cancer Genome Atlas (TCGA) and International Cancer Genome Consortium (ICGC) database. The univariate Cox proportional regression model was used to screen candidate autophagy-related prognostic genes. In addition, the multivariate Cox proportional regression model were helped to prove five key prognostic autophagy-related genes (ATIC, BAX, BIRC5, CAPNS1 and FKBP1A), which were used to construct prognostic signature. Results Based on the prognostic signature, liver cancer patients were significantly divided into high-risk and low-risk groups in terms of overall survival (OS). Further multivariate Cox regression analysis indicated that the prognostic signature remained as an independent prognostic factor for OS. The prognostic signature in possession of a better Area Under Curves (AUC) has a better performance in predicting the survival of patients with HCC, compared with other clinical parameters. Conclusion This study provides a prospective biomarker for monitoring the outcomes in the patients with HCC.

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