Dataset Information

A glycolysis-related gene pairs signature predicts prognosis in patients with hepatocellular carcinoma.

ABSTRACT:

Background

Hepatocellular carcinoma (HCC) is one of the most universal malignant liver tumors worldwide. However, there were no systematic studies to establish glycolysis?related gene pairs (GRGPs) signatures for the patients with HCC. Therefore, the study aimed to establish novel GRGPs signatures to better predict the prognosis of HCC.

Methods

Based on the data from Gene Expression Omnibus, The Cancer Genome Atlas (TCGA) and International Cancer Genome Consortium databases, glycolysis-related mRNAs were used to construct GRGPs. Cox regression was applied to establish a seventeen GRGPs signature in TCGA dataset, which was verified in two validation (European and American, and Asian) datasets.

Results

Seventeen prognostic GRGPs (HMMR_PFKFB1, CHST1_GYS2, MERTK_GYS2, GPC1_GYS2, LDHA_GOT2, IDUA_GNPDA1, IDUA_ME2, IDUA_G6PD, IDUA_GPC1, MPI_GPC1, SDC2_LDHA, PRPS1_PLOD2, GALK1_IER3, MET_PLOD2, GUSB_IGFBP3, IL13RA1_IGFBP3 and CYB5A_IGFBP3) were identified to be significantly progressive factors for the patients with HCC in the TCGA dataset, which constituted a GRGPs signature. The patients with HCC were classified into low-risk group and high-risk group based on the GRGPs signature. The GRGPs signature was a significantly independent prognostic indicator for the patients with HCC in TCGA (log-rank P = 2.898e-14). Consistent with the TCGA dataset, the patients in low-risk group had a longer OS in two validation datasets (European and American: P = 1.143e-02, and Asian: P = 6.342e-08). Additionally, the GRGPs signature was also validated as a significantly independent prognostic indicator in two validation datasets.

Conclusion

The seventeen GRGPs and their signature might be molecular biomarkers and therapeutic targets for the patients with HCC.

SUBMITTER: Zhou W

PROVIDER: S-EPMC7531359 | biostudies-literature | 2020

REPOSITORIES: biostudies-literature

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Publications

A glycolysis-related gene pairs signature predicts prognosis in patients with hepatocellular carcinoma.

Zhou Weige W Zhang Shijing S Cai Zheyou Z Gao Fei F Deng Wenhui W Wen Yi Y Qiu Zhen-Wen ZW Hou Zheng-Kun ZK Chen Xin-Lin XL

PeerJ 20200929

<h4>Background</h4>Hepatocellular carcinoma (HCC) is one of the most universal malignant liver tumors worldwide. However, there were no systematic studies to establish glycolysis‑related gene pairs (GRGPs) signatures for the patients with HCC. Therefore, the study aimed to establish novel GRGPs signatures to better predict the prognosis of HCC.<h4>Methods</h4>Based on the data from Gene Expression Omnibus, The Cancer Genome Atlas (TCGA) and International Cancer Genome Consortium databases, glyco ...[more]

PMID: 33062428

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Project description:Hepatocellular carcinoma (HCC) has been recognized as the third leading cause of cancer-related deaths worldwide. There is increasing evidence that the abnormal expression of autophagy-related genes plays an important role in the occurrence and development of HCC. Therefore, the study of autophagy-related genes can further elucidate the genetic drivers of cancer and provide valuable therapeutic targets for clinical treatment. In this study, we used 232 autophagy-related genes extracted from the Human Autophagy Database (HADb) and Molecular Signatures Database (MSigDB) to construct 1884 autophagy-related gene pairs. On this basis, we developed a prognostic model based on autophagy-related gene pairs using least absolute shrinkage and selection operator (LASSO) Cox regression to evaluate the prognosis of patients after liver cancer resection. We then used 845 liver cancer samples from three different databases to test the reliability of the risk signature through survival analysis, receiver operating characteristic (ROC) curve analysis, univariate and multivariate analysis. To further explore the underlying biological mechanisms, we conducted an enrichment analysis of autophagy-related genes. Finally, we combined the signature with independent prognostic factors to construct a nomogram. Based on the autophagy-related gene pair (ARGP) signature, we can divide patients into high- or low-risk groups. Survival analysis and ROC curve analysis verified the validity of the signature (AUC: 0.786-0.828). Multivariate Cox regression showed that the risk score can be used as an independent predictor of the clinical outcomes of liver cancer patients. Notably, this model has a more accurate predictive effect than most prognostic models for hepatocellular carcinoma. Moreover, our model is a powerful supplement to the HCC staging indicator, and a nomogram comprising both indicators can provide a better prognostic effect. Based on pairs of multiple autophagy-related genes, we proposed a prognostic model for predicting the overall survival rate of HCC patients after surgery, which is a promising prognostic indicator. This study confirms the importance of autophagy in the occurrence and development of HCC, and also provides potential biomarkers for targeted treatments.

Project description:Objective: Hepatocellular carcinoma (HCC) is a genetically and phenotypically heterogeneous tumor, and the prediction of its prognosis remains a challenge. In the past decade, studies elucidating the mechanisms that induce tumor cell pyroptosis has rapidly increased. The elucidation of their mechanisms is essential for the clinical development optimal application of anti-hepatocellular carcinoma therapeutics. Methods: Based on the different expression profiles of pyroptosis-related genes in HCC, we constructed a LASSO Cox regression pyroptosis-related genes signature that could more accurately predict the prognosis of HCC patients. Results: We identified seven pyroptosis-related genes signature (BAK1, CHMP4B, GSDMC, NLRP6, NOD2, PLCG1, SCAF11) in predicting the prognosis of HCC patients. Kaplan Meier survival analysis showed that the pyroptosis-related high-risk gene signature was associated with poor prognosis HCC patients. Moreover, the pyroptosis-related genes signature performed well in the survival analysis and ICGC validation group. The hybrid nomogram and calibration curve further demonstrated their feasibility and accuracy for predicting the prognosis of HCC patients. Meanwhile, the evaluation revealed that our novel signature predicted the prognosis of HCC patients more accurately than traditional clinicopathological features. GSEA analysis further revealed the novel signature associated mechanisms of immunity response in high-risk groups. Moreover, analysis of immune cell subsets with relevant functions revealed significant differences in aDCs, APC co-stimulation, CCR, check-point, iDCs, Macrophages, MHC class-I, Treg, and type II INF response between high- and low-risk groups. Finally, the expression of Immune checkpoints was enhanced in high-risk group, and m6A-related modifications were expressed differently between low- and high-risk groups. Conclusion: The novel pyroptosis-related genes signature can predict the prognosis of patients with HCC and insight into new cell death targeted therapies.

Project description:BackgroundHepatocellular carcinoma (HCC) is a global health burden with poor prognosis. Anoikis, a novel programmed cell death, has a close interaction with metastasis and progression of cancer. In this study, we aimed to construct a novel bioinformatics model for evaluating the prognosis of HCC based on anoikis-related gene signatures as well as exploring the potential mechanisms.Materials and methodsWe downloaded the RNA expression profiles and clinical data of liver hepatocellular carcinoma from TCGA database, ICGC database and GEO database. DEG analysis was performed using TCGA and verified in the GEO database. The anoikis-related risk score was developed via univariate Cox regression, LASSO Cox regression and multivariate Cox regression, which was then used to categorize patients into high- and low-risk groups. Then GO and KEGG enrichment analyses were performed to investigate the function between the two groups. CIBERSORT was used for determining the fractions of 22 immune cell types, while the ssGSEA analyses was used to estimate the differential immune cell infiltrations and related pathways. The "pRRophetic" R package was applied to predict the sensitivity of administering chemotherapeutic and targeted drugs.ResultsA total of 49 anoikis-related DEGs in HCC were detected and 3 genes (EZH2, KIF18A and NQO1) were selected out to build a prognostic model. Furthermore, GO and KEGG functional enrichment analyses indicated that the difference in overall survival between risk groups was closely related to cell cycle pathway. Notably, further analyses found the frequency of tumor mutations, immune infiltration level and expression of immune checkpoints were significantly different between the two risk groups, and the results of the immunotherapy cohort showed that patients in the high-risk group have a better immune response. Additionally, the high-risk group was found to have higher sensitivity to 5-fluorouracil, doxorubicin and gemcitabine.ConclusionThe novel signature of 3 anoikis-related genes (EZH2, KIF18A and NQO1) can predict the prognosis of patients with HCC, and provide a revealing insight into personalized treatments in HCC.

Dataset Information

A glycolysis-related gene pairs signature predicts prognosis in patients with hepatocellular carcinoma.

Background

Methods

Results

Conclusion

Publications

A glycolysis-related gene pairs signature predicts prognosis in patients with hepatocellular carcinoma.

Similar Datasets

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