Dataset Information

A Novel Signature Based on mTORC1 Pathway in Hepatocellular Carcinoma.

ABSTRACT: Background:mTORC1 signal pathway plays a role in the initiation and progression of hepatocellular carcinoma (HCC), but no relevant gene signature was developed. This research aimed to explore the potential correlation between the mTORC1 signal pathway and HCC and establish the related gene signature. Methods:HCC cases were retrieved from The Cancer Genome Atlas (TCGA), International Cancer Genome Consortium (ICGC), and Gene Expression Omnibus (GEO) databases. The genes included in mTORC1-associated signature were selected by performing univariate and multivariate Cox regression analyses and lasso regression analysis. The protein expression level of included genes was verified by The Human Protein Altas. Then, the signature was verified by survival analysis and multiple receiver operating characteristic (ROC) curve. Moreover, the correlation between signature and immune cells infiltration was investigated. Furthermore, a nomogram was established and evaluated by C-index and calibration plot. Results:The signature was established with the six genes (ETF1, GSR, SKAP2, HSPD1, CACYBP, and PNP). Three genes (ETF1, GSR, and HSPD1) have verified their protein expression level in HCC. Under the grouping from signature, patients in the high-risk group showed worse survival than those in the low-risk group in both three datasets. The signature was found to be significantly associated with the infiltration of B cells, CD4+ T-cells, CD8+ T-cells, dendritic cells, macrophages, and neutrophils. The univariate and multivariate Cox regression analysis indicated that mTORC1-related signature could be the potential independent prognostic factor in HCC. Finally, the nomogram involving age, gender, stage, and signature has been established and verified. Conclusion:The mTORC1-associated gene signature established and validated in our research could be used as a potential prognostic factor in HCC.

SUBMITTER: Mo Z

PROVIDER: S-EPMC7512110 | biostudies-literature | 2020

REPOSITORIES: biostudies-literature

ACCESS DATA

Publications

A Novel Signature Based on mTORC1 Pathway in Hepatocellular Carcinoma.

Mo Zhuomao Z Zhang Shuqiao S Zhang Shijun S

Journal of oncology 20200915

<h4>Background</h4>mTORC1 signal pathway plays a role in the initiation and progression of hepatocellular carcinoma (HCC), but no relevant gene signature was developed. This research aimed to explore the potential correlation between the mTORC1 signal pathway and HCC and establish the related gene signature.<h4>Methods</h4>HCC cases were retrieved from The Cancer Genome Atlas (TCGA), International Cancer Genome Consortium (ICGC), and Gene Expression Omnibus (GEO) databases. The genes included in ...[more]

PMID: 33014055

Similar Datasets

Project description:BackgroundPrognostic assessment is imperative for clinical management of patients with hepatocellular carcinoma (HCC). Most reported prognostic signatures are based on risk scores summarized from quantitative expression level of candidate genes, which are vulnerable against experimental batch effects and impractical for clinical application. We aimed to develop a robust qualitative signature to assess individual survival risk for HCC patients.MethodsLong non-coding RNA (lncRNA) pairs correlated with overall survival (OS) were identified and an optimal combination of lncRNA pairs based on the majority voting rule was selected as a classification signature to predict the overall survival risk in the cancer genome atlas (TCGA). Then, the signature was further validated in two external datasets. Besides, biomolecular characteristics, immune infiltration status, and chemotherapeutics efficacy of different risk groups were further compared. Finally, we performed key lncRNA screening and validated it in vitro.ResultsA signature consisting of 50 lncRNA pairs (50-LPS) was identified in TCGA and successfully validated in external datasets. Patients in the high-risk group, when at least 25 of the 50-LPS voted for high risk, had significantly worse OS than the low-risk group. Multivariate Cox, receiver operating characteristic (ROC) curve and decision curve analyses (DCA) demonstrated that the 50-LPS was an independent prognostic factor and more powerful than other available clinical factors in OS prediction. Comparison analyses indicated that different risk groups had distinct biomolecular characteristics, immune infiltration status, and chemotherapeutics efficacy. TDRKH-AS1 was confirmed as a key lncRNA and associated with cell growth of HCC.ConclusionsThe 50-LPS could not only predict the prognosis of HCC patients robustly and individually, but also provide theoretical basis for therapy. Besides, TDRKH-AS1 was identified as a key lncRNA in the proliferation of HCC. The 50-LPS might guide personalized therapy for HCC patients in clinical practice.

Project description:BackgroundHepatocellular carcinoma (HCC) has a poor prognosis and has become the sixth most common malignancy worldwide due to its high incidence. Advanced approaches to therapy, including immunotherapeutic strategies, have played crucial roles in decreasing recurrence rates and improving clinical outcomes. The HCC microenvironment is important for both tumour carcinogenesis and immunogenicity, but a classification system based on immune signatures has not yet been comprehensively described.MethodsHCC datasets from The Cancer Genome Atlas (TCGA), the Gene Expression Omnibus (GEO), and the International Cancer Genome Consortium (ICGC) were used in this study. Gene set enrichment analysis (GSEA) and the ConsensusClusterPlus algorithm were used for clustering assessments. We scored immune cell infiltration and used linear discriminant analysis (LDA) to improve HCC classification accuracy. Pearson's correlation analyses were performed to assess relationships between immune signature indices and immunotherapies. In addition, weighted gene co-expression network analysis (WGCNA) was applied to identify candidate modules closely associated with immune signature indices.ResultsBased on 152 immune signatures from HCC samples, we identified four distinct immune subtypes (IS1, IS2, IS3, and IS4). Subtypes IS1 and IS4 had more favourable prognoses than subtypes IS2 and IS3. These four subtypes also had different immune system characteristics. The IS1 subtype had the highest scores for IFNγ, cytolysis, angiogenesis, and immune cell infiltration among all subtypes. We also identified 11 potential genes, namely, TSPAN15, TSPO, METTL9, CD276, TP53I11, SPINT1, TSPO, TRABD2B, WARS2, C9ORF116, and LBH, that may represent potential immunological biomarkers for HCC. Furthermore, real-time PCR revealed that SPINT1, CD276, TSPO, TSPAN15, METTL9, and WARS2 expression was increased in HCC cells.ConclusionsThe present gene-based immune signature classification and indexing may provide novel perspectives for both HCC immunotherapy management and prognosis prediction.

Dataset Information

A Novel Signature Based on mTORC1 Pathway in Hepatocellular Carcinoma.

Publications

A Novel Signature Based on mTORC1 Pathway in Hepatocellular Carcinoma.

Similar Datasets

OmicsDI is part of the ELIXIR infrastructure

Tweets