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A Novel hepatocellular carcinoma specific hypoxic related signature for predicting prognosis and therapeutic responses.

ABSTRACT: Hypoxia is an important feature of the tumor microenvironment(TME) and is closely associated with cancer metastasis, immune evasion, and drug resistance. However, the precise role of hypoxia in hepatocellular carcinoma(HCC), as well as its influence on the TME, and drug sensitivity remains unclear. We found the excellent survival prediction value of Hypoxia_DEGs_Score model. In hypoxic HCC, somatic mutation, copy number variation, and DNA methylation were closely related to hypoxic changes and affected tumorigenesis, progression, metastasis, and drug resistance. In HCC, aggravated hypoxic stress was found to be accompanied by an immune exclusion phenotype and increased infiltration of immunosuppressive cells. In the validation cohort, patients with high Hypoxia_DEGs_Score were found to have worse immunotherapeutic outcomes and prognoses, and may benefit from drugs against cell cycle signaling pathways rather than those inhibiting the PI3K/mTOR pathway. Hypoxia_DEGs_Score has an excellent predictive capability of changes in the TME, the efficacy of immunotherapy, and the response of drugs. Therefore, Hypoxia_DEGs_Score can help develop personalized immunotherapy regimens and improve the prognosis of HCC patients.

SUBMITTER: Cai G

PROVIDER: S-EPMC9428591 | biostudies-literature | 2022

REPOSITORIES: biostudies-literature

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A Novel hepatocellular carcinoma specific hypoxic related signature for predicting prognosis and therapeutic responses.

Cai Guangzhen G Zhu Jinghan J Ning Deng D Li Ganxun G Zhang Yuxin Y Xiong Yixiao Y Liang Junnan J Yu Chengpeng C Chen Xiaoping X Liang Huifang H Ding Zeyang Z

Frontiers in immunology 20220817

Hypoxia is an important feature of the tumor microenvironment(TME) and is closely associated with cancer metastasis, immune evasion, and drug resistance. However, the precise role of hypoxia in hepatocellular carcinoma(HCC), as well as its influence on the TME, and drug sensitivity remains unclear. We found the excellent survival prediction value of Hypoxia_DEGs_Score model. In hypoxic HCC, somatic mutation, copy number variation, and DNA methylation were closely related to hypoxic changes and a ...[more]

PMID: 36059442

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Project description:BackgroundHepatocellular carcinoma (HCC) often has an insidious onset and rapid progression. Often, when the disease is first diagnosed, the opportune time for surgical intervention has already lapsed. In addition, the effects of systemic treatment is relatively unsatisfactory. Metabolic reprogramming is one of the hallmarks of cancer. This study aimed to identify a set of genes related to metabolism to construct a predictive model for the prognosis of HCC.MethodsThe transcriptomic and clinical data of 352 HCC patients were obtained from The Cancer Genome Atlas (TCGA) Liver Hepatocellular Carcinoma (LIHC) dataset and divided into a training cohort (n=212) and a testing cohort (n=140) at a ratio of 6:4. Univariate Cox regression analysis and the LASSO Cox regression model were used to identify 5 genes to establish a risk score for predicting the prognosis of HCC patients. Subsequently, the molecular characteristics of the model were assessed and the ability of the model to predict the tumor immune microenvironment and patient response to immunotherapy and chemotherapy was also examined.ResultsThe risk score model was constructed based on the five genes, methyltransferase-like protein 6 (METTL6), RNA polymerase III subunit G (POLR3G), phosphoribosyl pyrophosphate amidotransferase (PPAT), SET Domain Bifurcated 2 (SETDB2), and suppressor of variegation 3-9 homolog 2 (SUV39H2). The Kaplan-Meier survival analysis and time-dependent receiver operating characteristic (ROC) curves demonstrated that high-risk patients had a poorer overall survival (OS) compared to low-risk patients. he nomogram score had a better predictive ability compared to the common factors. Our results finally showed that high-risk cases were associated with cell proliferation and cell cycle related gene sets, high tumor protein P53 (TP53) mutation rate, suppressive immunity and increased sensitivity to cisplatin, gemcitabine and docetaxel. Meanwhile, low-risk cases were associated with cell cycle and immune response related pathways, low TP53 mutation rate, active immunity and more benefit from immunotherapy.ConclusionsThis study provided novel insights into the role of metabolism-related genes in HCC, and demonstrated that our model could be a promising prognostic biomarker for distinguishing the molecular and immune characteristics and inferring the potential response to chemotherapy and immunotherapy.

Project description:BackgroundProgrammed cell death (PCD) functions critically in cancers and PCD-related genes are associated with tumor microenvironment (TME), prognosis and therapeutic responses of cancer patients. This study stratified hepatocellular carcinoma (HCC) patients and develop a prognostic model for predicting prognosis and therapeutic responses.MethodsConsensus clustering analysis was performed to subtype HCC patients in The Cancer Genome Atlas (TCGA) database. Differentially expressed genes (DEGs) among the subtypes were filtered and subjected to the least absolute shrinkage and selection operator (LASSO) regression analysis and univariate Cox regression analysis to filter prognostic genes. A PCD-related prognostic gene signature in TCGA was constructed and validated in ICGC-LIRI-JP and GSE14520 datasets. TME was analyzed using CIBERSORT, MCP-counter, TIMER and EPIC algorithms. Drug sensitivity was predicted by oncoPredict package. Spearman analysis was used to detect correlation.ResultsFour molecular subtypes were categorized based on PCD-related genes. Subtype C1 showed the poorest prognosis, the most infiltration of Fibroblasts, dentritic cell (DC) and cancer-associated fibroblasts (CAFs), and the highest TIDE score. C4 had a better prognosis survival outcome, and lowest immune cell infiltration. The survival outcomes of C2 and C3 were intermediate. Next, a total of 69 co-DEGs were screened among the four subtypes and subsequently we identified five prognostic genes (MCM2, SPP1, S100A9, MSC and EPO) for developing the prognostic model. High-risk patients not only had unfavorable prognosis, higher clinical stage and grade, and more inflammatory pathway enrichment, but also possessed higher possibility of immune escape and were more sensitive to Cisplatin and 5. Fluorouracil. The robustness of the prognostic model was validated in external datasets.ConclusionThis study provides new insights into clinical subtyping and the PCD-related prognostic signature may serve as a useful tool to predict prognosis and guide treatments for patients with HCC.

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Project description:BackgroundLong noncoding RNAs (lncRNAs) are found to be novel biomarkers for hepatocellular carcinoma (HCC) and play an important role in tumor progression. We established a genomic instability-related long noncoding RNA signature (GIlncSig) as an independent prognosis factor and also investigated its impact on prognosis significance.MethodSomatic mutation profiles, clinical characteristics, and RNA sequencing data were obtained from The Cancer Genome Atlas (TCGA) database. Lasso regression was used to construct GIlncSig. KEGG was used to identify the possible biological pathways. ESTIMATE and CIBERSORT algorithms were used to calculate the immune microenvironment scores and proportion of immune cells in HCC patients. The expression of LINC00501 was conducted by qRT-PCR. Cell proliferation was measured by EdU, CCK-8, and colony formation assay, and cell migration and invasion ability were measured by wound healing and transwell assay.Results135 genomic instability-related lncRNAs were identified, and GIlncSig was constructed using 13 independent lncRNAs with significant prognosis values. Based on the GIlncSig, high-risk group had worse clinical outcomes than low-risk group, while high-risk group also had higher UBQLN4, KRAS, ARID1A, and PIK3CA expression. Moreover, the efficiency of GIlncSig combining single-gene mutation was higher than single-gene mutation alone such as TP53. The results of CIBERSORT and ESTIMATE showed that GS group and GU group had significantly different immune infiltration. In addition, LINC00501 was identified as a potential biomarker in HCC with strong relationship with clinical characteristics. In vitro assays validated that LINC00501 promoted proliferation and migration of HCC cell lines.ConclusionOur results showed that GIlncSig serves as a potential independent prognosis factor to predict HCC patients' prognosis for exploring potential mechanism and therapy strategy. Besides, LINC00501 plays an important role in the progression of HCC, which may be a potential therapy target.

Dataset Information

A Novel hepatocellular carcinoma specific hypoxic related signature for predicting prognosis and therapeutic responses.

Publications

A Novel hepatocellular carcinoma specific hypoxic related signature for predicting prognosis and therapeutic responses.

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