The proteomic characterization of the peritumor microenvironment in human hepatocellular carcinoma

Gu, Yijun; Guo, Yuanyuan; Gao, Na; Fang, Yan; Xu, Chen; Hu, Guiming; Guo, Mengxue; Ma, Yaxing; Zhang, Yunfei; Zhou, Jun; Luo, Yanlin; Zhang, Haifeng; Wen, Qiang; Qiao, Huan

doi:10.1038/s41388-022-02264-3

Cited by 19 publications

(12 citation statements)

References 60 publications

(63 reference statements)

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“…Although targeted molecular therapies and immunotherapy have demonstrated encouraging treatment benefits for HCC patients recently, and atezolizumab and bevacizumab are used as first-line treatment for advanced HCC [ 42 ], the overall clinical prognosis of HCC patients are far from satisfactory as a consequence of drug resistance [ 1 , 4 ]. Many studies were performed to explore effective models to precisely predict the prognosis and response to treatment of HCC patients, in the hope of providing more evidence for precision therapy [ 43 , 44 , 45 ]. Therefore, it is imperative to detect novel validated prognostic biomarkers and construct new models to predict the precise treatment for HCC patients.…”

Section: Discussionmentioning

confidence: 99%

Identification of Prognosis-Related Oxidative Stress Model with Immunosuppression in HCC

et al. 2023

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For hepatocellular carcinoma (HCC) patients, we attempted to establish a new oxidative stress (OS)-related prognostic model for predicting prognosis, exploring immune microenvironment, and predicting the immunotherapy response. Significantly differently expressed oxidative stress-related genes (DEOSGs) between normal and HCC samples from the Cancer Genome Atlas (TCGA) were screened, and then based on weighted gene coexpression network analysis (WGCNA), HCC-related hub genes were discovered. Based on the least absolute shrinkage and selection operator (LASSO) and cox regression analysis, a prognostic model was developed. We validated the prognostic model’s predictive power using an external validation cohort: the International Cancer Genome Consortium (ICGC).Then a nomogram was determined. Furthermore, we also examined the relationship of the risk model and clinical characteristics as well as immune microenvironment. 434 DEOSGs, comprising 62 downregulated and 372 upregulated genes (p < 0.05 and |log2FC| ≥ 1), and 257 HCC-related hub genes were recognized in HCC. Afterward, we built a five-DEOSG (LOX, CYP2C9, EIF2B4, EZH2, and SRXN1) prognostic risk model. Using the nomogram, the risk model was shown to have good prognostic value. Compared to the low risk group, HCC patients with high risk had poorer outcomes, worse pathological grades, and advanced tumor stages (p < 0.05). There were significant increases in LOX, EIF2B4, EZH2, and SRXN1 expression in HCC samples, while CYP2C9 expression was decreased. Finally, Real-time PCR (RT-qPCR) confirmed the mRNA expressions of five genes (CYP2C9, EIF2B4, EZH2, SRXN1, LOX) in HCC cell lines. Our study constructed a prognostic OS-related model with strong predictive power and potential as an immunosuppressive biomarker for HCC leading to improving prediction and providing new insights for HCC immunotherapy.

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Section: Discussionmentioning

confidence: 99%

Identification of Prognosis-Related Oxidative Stress Model with Immunosuppression in HCC

et al. 2023

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“…Although phosphopeptides, ordinary tryptic peptides, metabolites, and lipids are all detected by LC-MS/MS, at a practical level, phosphoproteomics is considerably more complex and challenging to implement for clinical samples than other omics. Previous studies have identified over 20,000 phosphopeptides or phosphorylation sites in HCC tumors using high-pH reverse phase fractionation (one sample, six injections) and TiO2 enrichment [23][24][25][26][27][28][29][30][31][32][33][34]. Taking advantage of the high sensitivity and speed of the timsTOF Pro 2 mass spectrometer and the complementary results of IMAC Fe-NTA and TiO2 profiling [45][46][47], a comparable number of phosphopeptides has been obtained using a much simpler approach (one sample, two injections; routine DDA data acquisition without projectspecific proteomics libraries).…”

Section: Discussionmentioning

confidence: 99%

“…Due to the heterogeneity of tumor tissue, omic studies in this research could not identify specific cell types. Although this limitation might be partially addressed by bioinformatic assays [25], elaborate biological experiments are needed for validation. Additionally, glycogen synthase kinase 3α (GSK3A) was hyperactivated in advanced-stage HCC, which is responsible for glycogen accumulation and tumorigenesis by the suppression of Hippo signaling through glycogen-induced phase separation [63].…”

Section: Discussionmentioning

confidence: 99%

“…Early proteomic work mostly focused on identifying prognostic biomarkers in serum or tumor tissue samples [21], while recent research has employed proteomics, phosphoproteomics, and AI-based data interpretation to uncover more mechanisms underlying HCC development and progression. This has led to the discovery of novel therapeutic targets, such as SOAT1, Src family kinase, PCNA, CDK1, FGFR4, MFAP5, etc [22][23][24][25][26][27].…”

Section: Introductionmentioning

confidence: 99%

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Comprehensive Multi-Omic Analysis Reveals Distinct Molecular Features in Early and Advanced Stages of Hepatocellular Carcinoma

Fan,

Hu,

et al. 2023

Preprint

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Hepatocellular Carcinoma (HCC) is a serious primary solid tumor that is prevalent worldwide. Due to its high mortality rate, it is crucial to explore both early diagnosis and advanced treatment for HCC. In recent years, multi-omic approaches have emerged as promising tools to identify biomarkers and investigate molecular mechanisms of biological processes and diseases. In this study, we performed proteomics, phosphoproteomics, metabolomics, and lipidomics to reveal the molecular features of early- and advanced-stage HCC. The data obtained from these omics were analyzed separately and then integrated to provide a comprehensive understanding of the disease. The multi-omic results unveiled intricate biological pathways and interaction networks underlying the initiation and progression of HCC. Moreover, we proposed specific potential biomarker panels for both early- and advanced-stage HCC by overlapping our data with CPTAC database, and deduced novel insights and mechanisms related to HCC origination and development.

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“…Gu et al proposed a new concept of the peritumor microenvironment (PME) and explored the proteomic characterization of the PME for occurrence (PME-O) and progression (PME-P) in human hepatocellular carcinoma (HCC) [45]. Immunity was found essential in PME-O, while inflammation, angiogenesis, and metabolism were critical in PME-P. Proteome profiling identified thymidine phosphorylase (TYMP) as an antiangiogenic target in the HCC mouse model.…”

Section: Proteomicsmentioning

confidence: 99%

An overview of multiomics: a powerful tool applied in cancer molecular subtyping for cancer therapy

Zou,

Zhao,

Song

2023

Malignancy Spectrum

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During the process of carcinogenesis and tumor progression, various molecular alternations occur in different omics levels. In recent years, multiomics approaches including genomics, epigenetics, transcriptomics, proteomics, metabolomics, single‐cell omics, and spatial omics have been applied in mapping diverse omics profiles of cancers. The development of high‐throughput technologies such as sequencing and mass spectrometry has revealed different omics levels of tumor cells or tissues separately. While focusing on a single omics level results in a lack of accuracy, joining multiple omics approaches together undoubtedly benefits accurate molecular subtyping and precision medicine for cancer patients. With the deepening of tumor research in recent years, taking pathological classification as the only criterion of diagnosis and predicting prognosis and treatment response is found to be not accurate enough. Therefore, identifying precise molecular subtypes by exploring the molecular alternations during tumor occurrence and development is of vital importance. The review provides an overview of the advanced technologies and recent progress in multiomics applied in cancer molecular subtyping and detailedly explains the application of multiomics in identifying cancer driver genes and metastasis‐related genes, exploring tumor microenvironment, and selecting liquid biopsy biomarkers and potential therapeutic targets.

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The proteomic characterization of the peritumor microenvironment in human hepatocellular carcinoma

Cited by 19 publications

References 60 publications

Identification of Prognosis-Related Oxidative Stress Model with Immunosuppression in HCC

Identification of Prognosis-Related Oxidative Stress Model with Immunosuppression in HCC

Comprehensive Multi-Omic Analysis Reveals Distinct Molecular Features in Early and Advanced Stages of Hepatocellular Carcinoma

An overview of multiomics: a powerful tool applied in cancer molecular subtyping for cancer therapy

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