Development and validation of a coagulation-related genes prognostic model for hepatocellular carcinoma

Yang, Wan-Xia; Gao, Hong‐Wei; Cui, Jia-Bo; Zhang, An-An; Wang, Fangfang; Xie, Jianqin; Lu, Minghua; You, Chongge

doi:10.1186/s12859-023-05220-4

Cited by 2 publications

(1 citation statement)

References 35 publications

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“…In order to further screen gene sets related to FAM subtypes, we used the limma package to analyze the differences between C1 and C2+C3, C2 and C1+C3, and C3 and C1+C2 in the TCGA-LUAD dataset under the threshold of | log2 (a Fold Change) | > 1, FDR <0.05 ( Ritchie et al, 2015 ). GO and KEGG enrichment analysis were performed on genes showing abnormal expression using the clusterProfiler software package ( Yang et al, 2023 ).…”

Section: Methodsmentioning

confidence: 99%

Applying machine learning algorithms to develop a survival prediction model for lung adenocarcinoma based on genes related to fatty acid metabolism

Cong,

Zhao,

Zhang

et al. 2023

Front. Pharmacol.

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Background: The progression of lung adenocarcinoma (LUAD) may be related to abnormal fatty acid metabolism (FAM). The present study investigated the relationship between FAM-related genes and LUAD prognosis.Methods: LUAD samples from The Cancer Genome Atlas were collected. The scores of FAM-associated pathways from the Kyoto Encyclopedia of Genes and Genomes website were calculated using the single sample gene set enrichment analysis. ConsensusClusterPlus and cumulative distribution function were used to classify molecular subtypes for LUAD. Key genes were obtained using limma package, Cox regression analysis, and six machine learning algorithms (GBM, LASSO, XGBoost, SVM, random forest, and decision trees), and a RiskScore model was established. According to the RiskScore model and clinical features, a nomogram was developed and evaluated for its prediction performance using a calibration curve. Differences in immune abnormalities among patients with different subtypes and RiskScores were analyzed by the Estimation of STromal and Immune cells in MAlignant Tumours using Expression data, CIBERSORT, and single sample gene set enrichment analysis. Patients’ drug sensitivity was predicted by the pRRophetic package in R language.Results: LUAD samples had lower scores of FAM-related pathways. Three molecular subtypes (C1, C2, and C3) were defined. Analysis on differential prognosis showed that the C1 subtype had the most favorable prognosis, followed by the C2 subtype, and the C3 subtype had the worst prognosis. The C3 subtype had lower immune infiltration. A total of 12 key genes (SLC2A1, PKP2, FAM83A, TCN1, MS4A1, CLIC6, UBE2S, RRM2, CDC45, IGF2BP1, ANGPTL4, and CD109) were screened and used to develop a RiskScore model. Survival chance of patients in the high-RiskScore group was significantly lower. The low-RiskScore group showed higher immune score and higher expression of most immune checkpoint genes. Patients with a high RiskScore were more likely to benefit from the six anticancer drugs we screened in this study.Conclusion: We developed a RiskScore model using FAM-related genes to help predict LUAD prognosis and develop new targeted drugs.

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Section: Methodsmentioning

confidence: 99%

Applying machine learning algorithms to develop a survival prediction model for lung adenocarcinoma based on genes related to fatty acid metabolism

Cong,

Zhao,

Zhang

et al. 2023

Front. Pharmacol.

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Protease activated receptor 2 as a novel druggable target for the treatment of metabolic dysfunction-associated fatty liver disease and cancer

Villano,

Pontisso

2024

Front. Immunol.

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Metabolic dysfunction-associated fatty liver disease (MAFLD) is spreading worldwide, largely due to unhealthy lifestyles that contribute to the rise in diabetes, metabolic syndrome, and obesity. In this situation, the progression of injury to metabolic steatohepatitis can evolve to cirrhosis and, eventually, to hepatocellular carcinoma (HCC). It is well known that serine protease enzymes with different functions in cellular homeostasis act as signaling molecules that regulate liver inflammation by activating the protease-activated receptors (PARs) family members, expressed on the cellular plasma membrane. Among them, PAR2 plays a central role in the activation of signaling pathways in response to changes in the extracellular microenvironment. Experimental data have provided evidence that PAR2 is involved not only in inflammatory response but also in insulin resistance, lipid metabolism, and cancer. The major aims of this narrative review are addressed to assess PAR2 involvement in inflammation, metabolism, and liver disease progression and to explore possible therapeutic strategies, based on PAR2 inhibition, in order to prevent its biological effects in the context of MAFLD and cancer.

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Development and validation of a coagulation-related genes prognostic model for hepatocellular carcinoma

Cited by 2 publications

References 35 publications

Applying machine learning algorithms to develop a survival prediction model for lung adenocarcinoma based on genes related to fatty acid metabolism

Applying machine learning algorithms to develop a survival prediction model for lung adenocarcinoma based on genes related to fatty acid metabolism

Protease activated receptor 2 as a novel druggable target for the treatment of metabolic dysfunction-associated fatty liver disease and cancer

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