Dataset Information

A signature of 33 immune-related gene pairs predicts clinical outcome in hepatocellular carcinoma.

ABSTRACT: OBJECTIVE:Hepatocellular carcinoma (HCC) has become the second most common tumor type that contributes to cancer-related death worldwide. The study aimed to establish a robust immune-related gene pair (IRGP) signature for predicting the prognosis of HCC patients. METHODS:Two RNA-seq datasets (The Cancer Genome Atlas Program and International Cancer Genome Consortium) and one microarray dataset (GSE14520) were included in this study. We used a series of immune-related genes from the ImmPort database to construct gene pairs. Lasso penalized Cox proportional hazards regression was employed to develop the best prognostic signature. We assigned patients into two groups with low immune risk and high immune risk. Then, the prognostic ability of the signature was evaluated by a log-rank test and a Cox proportional hazards regression model. RESULTS:After 1000 iterations, the 33-immune gene pair model obtained the highest frequency. As a result, we chose the 33 immune gene pairs to establish the immune-related prognostic signature. As we expected, the immune-related signature accurately predicted the prognosis of HCC patients, and high-risk groups showed poor prognosis in the training datasets and testing datasets as well as in the validation datasets. Furthermore, the immune-related gene pair (IRGP) signature also showed higher predictive accuracy than three existing prognostic signatures. CONCLUSION:Our prognostic signature, which reflects the link between the immune microenvironment and HCC patient outcome, is promising for prognosis prediction in HCC.

SUBMITTER: Sun XY

PROVIDER: S-EPMC7163092 | biostudies-literature | 2020 Apr

REPOSITORIES: biostudies-literature

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A signature of 33 immune-related gene pairs predicts clinical outcome in hepatocellular carcinoma.

Sun Xiao-Yan XY Yu Shi-Zhe SZ Zhang Hua-Peng HP Li Jie J Guo Wen-Zhi WZ Zhang Shui-Jun SJ

Cancer medicine 20200218 8

<h4>Objective</h4>Hepatocellular carcinoma (HCC) has become the second most common tumor type that contributes to cancer-related death worldwide. The study aimed to establish a robust immune-related gene pair (IRGP) signature for predicting the prognosis of HCC patients.<h4>Methods</h4>Two RNA-seq datasets (The Cancer Genome Atlas Program and International Cancer Genome Consortium) and one microarray dataset (GSE14520) were included in this study. We used a series of immune-related genes from th ...[more]

PMID: 32068352

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Project description:ObjectiveMelanoma is rare but dangerous skin cancer, and it can spread rather quickly in the advanced stages of the tumor. Abundant evidence suggests the relationship between tumor development and progression and the immune system. A robust gene risk model could provide an accurate prediction of clinical outcomes. The present study aimed to explore a robust signature of immune-related gene pairs (IRGPs) for estimating overall survival (OS) in malignant melanoma.MethodsClinical and genetic data of skin cutaneous melanoma (SKCM) patients from The Cancer Genome Atlas (TCGA) was performed as a training dataset to identify candidate IRGPs for the prognosis of melanoma. Two independent datasets from the Gene Expression Omnibus (GEO) database (GSE65904) and TCGA dataset (TCGA-UVM) were selected for external validation. Univariate and multivariate Cox regression analyses were then performed to explore the prognostic power of the IRGPs signature and other clinical factors. CIBERSORTx was applied to estimate the fractions of infiltrated immune cells in bulk tumor tissues.ResultsA signature consisted of 33 IRGPs was established which was significantly associated with patients' survival in the TCGA-SKCM dataset (P = 2.0×10-16, Hazard Ratio (HR) = 4.220 (2.909 to 6.122)). We found the IRGPs signature exhibited an independent prognostic factor in all the three independent cohorts in both the univariate and multivariate Cox analysis (P<0.01). The prognostic efficacy of the signature remained unaffected regardless of whether BRAF or NRAS was mutated. As expected, the results were verified in the GSE65904 dataset and the TCGA-UVM dataset. We found an apparent shorter OS in patients of the high-risk group in the GSE65904 dataset (P = 2.1×10-3; HR = 1.988 (1.309 to 3.020)). The trend in the results of the survival analysis in TCGA-UVM was as we expected, but the result was not statistically significant (P = 0.117, HR = 4.263 (1.407 to 12.91)). CD8 T cells, activated dendritic cells (DCs), regulatory T cells (Tregs), and activated CD4 memory T cells presented a significantly lower fraction in the high-risk group in the TCGA-SKCM dataset(P <0.01).ConclusionThe results of the present study support the IRGPs signature as a promising marker for prognosis prediction in melanoma.

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 signature of 33 immune-related gene pairs predicts clinical outcome in hepatocellular carcinoma.

Publications

A signature of 33 immune-related gene pairs predicts clinical outcome in hepatocellular carcinoma.

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